rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix A. Pilots combining the output approach and the process approach in developing countries**

The ILO carried out two interesting pilot surveys to test the output and process approaches in developing countries. These pilots were deployed in Albania and Mongolia.<sup>64</sup> The instrument differed to those of the BLS in three ways. First, instead of a standalone survey the pilots were thought of as modules that could be attached to already established surveys. Second, while the U.S. examples are both enterprise surveys, the ILO piloted an enterprise module and a household module to be attached to already established surveys—the enterprise survey (ES) in Albania and the national labor force survey (LFS) in Mongolia. The reason for testing a household module was that in developing countries the informal sector, the self-employed, and the agriculture sector are a large part of the economy and are usually not captured in enterprise surveys. Third, the pilots included in both modules a combination of questions from both the GGS and the GTP surveys, with some adaptations to developing countries. For example, the instrument includes sustainable and organic agriculture in a separate category to the GTP conserving natural resources. Despite modules parallelism, results are not comparable due to differences in sampling frames and the underlying differences in each of the surveys. For example, the Albanian Enterprise Survey (ES) did not include the agriculture sector while the labor force survey (LFS) did, and the Mongolian ES did not include the banking and financial sector, private health sector, and government agencies while the LFS did.

Although the pilots have limitations natural to the approaches, they also present some advantages. First, by including in a single firm survey instrument questions on outputs and processes, jobs can be added up to get a single number of jobs (those involved in the production of an EGS and/or those involved in environmentally friendly processes), without the issue of double counting. Second, since the household module is
to its sampling design, the majority of our students completed four years of high school. This is as expected, since even TEEP students (grade 6 in school years 2004/05 and 2005/06) are expected to reach age 17 in the survey period (note that children graduate from high school at age 15 or 16 if they have no delays or repetitions in schooling).

The tracking survey captured information on schooling and work histories in detail. Since our sample students were grade-6 enrollers in particular school years, we omit questions on their schooling histories up to that stage. Detailed information starts from high school entry and onward. Similar to the sibling section of the household survey, we collected information on school (name and school ID), school type (public or private), age started, graduated or not, age graduated, age

> 22There were 36 cases untracked out of the total of 3,487 students being tracked. Therefore, the tracking rate is about 98.97 percent.

17
**Globally, women’s life expectancy at birth exceeds that of men by nearly five years, and the gap persists into older ages** . The gender longevity gap is highest in Latin America, Europe, and Eastern and South-Eastern Asia. This gender gap suggests that productive aging policies need to pay specific attention to skills and labor market constraints for women, and health constraints for men.

**Figure 1: Demographic changes differ across regions, but aging is coming everywhere**

<!-- Start of picture text --> Share of 65+ in the population across % change in population 65+, 2019-2050<br>20 regions, 2020 250<br>18 200<br>16<br>14 150<br>12<br>100<br>10<br>8 50<br>6<br>4 0<br>2<br>% of population aged 65+ 0<br>% change in pop 65+ Oceania<br>Africa<br>Sub-Saharan Central and<br>Sub-Saharan Africa Middle East & North Africa South Asia East Asia & Pacific Europe & Central Asia OECD members Low income Middle income High income Northern Africa and Western Asia Southern Asia Latin America and the Caribbean Eastern and South-Eastern Asia Europe and Northern America<br><!-- End of picture text --> Source: World Development Indicators (left), UN 2019 (right).

**The priorities and space for reforms will differ between less and more advanced economies, reflecting different contexts of aging and productive work.** Most analysis and evidence on aging societies and the relationship with longer working lives is based on HICs. Much can be learned from these experiences, but there are important differences between less and more advanced economies that affect priorities as well as the available and desirable set of policy instruments that can support longer and more productive working lives. These include, _inter alia_ , labor market structure, access to social protection
available evidence on the provinces' experiences to date. The Fondo Fiduciario had balance sheets for only three residual entities both at their inception and at some point later (when asset recovery and liability retirement should have already begun). Unfortunately, the balance sheet data for these three cases do not give any indications as to the terms on which the assets were recovered or liabilities retired. Assets may have been recovered at a rate of pennies on the dollar, while liabilities may have been retired at face value.

**Table 5: Fondo Fiduciario Assistance**

|Bank|Residual<br>Liabilities<br>(millions)|Total Loan<br>from FF<br>(millions)<sup>27</sup>|Undisbursed<br>as of 2/4/97|Debts Paid to<br>B.C.R.A.|Debts Paid to<br>Banco De La<br>Nacion|
|---|---|---|---|---|---|
|Chaco|233.1|78.0|0|78.0|0|
|Entre Rios|N/A.|78.0|0|45.3|0|
|Formosa|244.9|80.0|0|32.2|37.7|
|Jujuy|N/A.|50.0|33.3|16.6|0|
|Mendoza|666.6|160.0|0|89.7|70.3|
|Prv Soc Men|292.1|100.0|0|79.3|20.7|
|Misiones|331.8|78.0|0|20.2|47.6|
|Rio Negro|47.4|80.0|0|32.3|11.5|
|<br>Salta
>**hyperbolic sine**|y = ln (x+ √x<sup>2</sup> + 1<br>)|5|
|**Square root**|y= √x|6|

Sources: [1,2 and 6] any math textbook; [3] Box and Cox (1964); [4] Yeo and Johnson (2000); [5] Friedline et al. (2014).

Each of the transformations in Table 1 has properties that fit different needs. For example, while the natural log may only be applied to strictly positive variables, log10 (x+ a) takes care of negative and zero values, too. Similarly, while the use of Box-Cox is limited to strictly positive variables, the Yeo-Johnson and the inverse hyperbolic transformations apply to all variables and perform relatively better in the presence of highly skewed distributions (which makes them particularly suitable for the analysis household wealth data). A general criterion to select the “best” transformation is goodness-of-fit: the transformation that provides the best approximation to a Normal distribution for the specific variable and data set in use will be the best choice for that particular context. `outdetect` allows the user to maximize goodness-of-fit relying on the Pearson chisquared test (Snedecor and Cochran, 1989). As pointed out by Peterson and Cavanaugh (2019), the Pearson statistic, P, divided by its degrees of freedom, df, converges to 1 when the data approaches a Gaussian distribution: Pdf⁄ can be interpreted as a measure of how close a distribution is to normality, and used to rank transformations according to how successful they are in normalizing the data.

7
Shorter run studies do not find this to be the case.

# C. _Quid Pro Quo Lobbying_

In contrast to Hansen (1991), Grossman and Helpman (1994) put forth a theory of how policy responds to lobbying contributions. In their model, money causes votes and is the source of electoral strength. A stylized fact that supports this view is that lobbying contributions account for a significant share, up to 80%, of a congress person’s total campaign

> 3 This finding about Ways and Means is at odds with Wright’s findings from his survey of lobbies.

11
to co-locate functions so as to flexibly respond to fast changing niche markets exist in a state of tension with national land policies and high central city land prices. The central government has prohibited construction of new manufacturing plants and university campuses in Tokyo's urban core to alleviate congestion. Steep land prices also make building large-scale R&D centers in central Tokyo all but prohibitive. Tokyo firms have tried to optimize the conflicting pressures, first, by locating new R&D centers outside but close to the central city, and second, by turning their existing high volume manufacturing plants in Tokyo into R&D linked mother plants performing pilot production of prototypes, and relocating their mass production facilities further out in greater Tokyo and elsewhere in Japan. A recent government survey of location preferences among Japanese firms demonstrates their 18
in place.</mark>

**<mark>This growth diagnostics exercise will hence identify constraints that prevent a shift from current stagnation towards a desired state of rapid, converging growth in Togo</mark>** <mark>. This is not to say that all countries in a given state will have to follow similar recipes (as the extrapolation of lessons is precisely what GD is</mark> _<mark>not</mark>_ <mark>about) but, as Pritchett explains “the set of diagnostics and therapeutics recommended are likely to be more similar across countries starting from similar states”.</mark>

14 As Hausmann et al (2008) explain: “The surviving firms may be politically well connected; form part of financial conglomerates; or be exempt from taxation because of some specific provision, making them less sensitive to problems of governance, finance or taxation, respectively”.

15 For cultural reasons, what is outrageous corruption in Sweden may not raise too many eyebrows elsewhere (Hausmann et al. 2008). Also, a specific growth determinant such as education may be more important in the US than in Zambia due to their different economic structures.

16 For instance, Togo’s business enterprise survey was restricted to firms in Lomé. Also, the agricultural sector or the informal sector is normally not surveyed, posing problems for lower income countries that rely mostly upon primary activities. 17 The six possible growth states according to Pritchett (2008) are: (i) poverty traps, (ii) a status of economic decline or collapse, (iii) stagnation (or non converging growth), (iv) modest converging growth, (v) rapid converging growth and (vi) growth leaders.

9
inflation. The models will be applied to changes in livelihoods and determinants of the impact mainly at the household level. Since different causes affected livelihoods in this period of instability in South Sudan, the difference-in-difference approach will identify the effect of inflation on livelihoods by correlating changes in prices with changes in livelihood indicators. The data sets contain information on security, economic conditions, education, employment, access to services, and perceptions. They also include comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption Survey methodology as detailed in Pape and Mistiaen (2018).

Table 1: High Frequency South Sudan Survey (HFSSS), survey dates and coverage

||Data collection<br>dates|Geographic coverage|Rural/Urban coverage|
|---|---|---|---|
|Wave 1|February 2015 -<br>September 2015|6 out of 10 states: Western Equatoria,<br>Central Equatoria, Eastern Equatoria,<br>Northern Bahr El Ghazal, Western Bahr<br>El Ghazal, and Lakes state.|Covered urban and rural<br>households|
|Wave 2|February 2016 –<br>June 2016|7 out of 10 states: wave 1 + Warrap<br>state. The other three former states<br>(Jonglei, Unity, and Upper Nile) could<br>not be surveyed due to security concerns.|Revisited urban<br>households interviewed in<br>Wave 1|
|Wave 3|September 2016 -<br>March 2017|7 out of 10 states: Same as Wave 2|Covered a new cross-<br>section of urban and rural<br>households|
|Wave 4|May 2017 -<br>August 2017|7 out of 10 states: Same as Waves 2 and<br>3.|Revisited urban<br>households from Waves 1<br>and 2|

Source:
Specifically, we assess how share price returns respond to news about president Wahid’s potential impeachment<sup>27</sup> by running regressions of the following form: i i Where i is the return on the price of security iiVV _I_ during episode _e,_ P i is a measure of political RR = αα+ ρρP LL + εε connectedness, notably the Suharto Dependency Index developed by the Castle Group, and i is an error RR LL term. The coefficient on _POL_ should be negative if political connections remained relevant. We also run εε regressions in which we augment the specification with an indicator of the return on the Jakarta Stock Exchange Composite Index net of broader Southeast Asian Effects (referred to as _NR JCI_ ) and its interaction with the indicator of political connectedness. _NR JCI_ serves as a measure of event severity. If the severity of an adverse rumor affects politically dependent more than less dependent firms the interaction term _NR JCI*POL_ should be positive.<sup>28</sup> To set the scene for the analysis Figure B1a shows the response of share prices, as measured by average daily returns, by level of political connectedness to rumors about Suharto’s health; connected firms experienced greater reductions in their share prices than firms less dependent on Suharto. Such a pattern is not present in Figure B1b, which examines share prices responses to six salient events leading up to Wahid’s impeachment.

The 51 surviving firms we use for our analysis of share prices responses to political news in the post-Suharto era do not seem to be systematically more or less connected than the original sample of 79 firms used by Fisman as is shown in Table B1 below. Since these 51 firms operate in different sectors, as is shown in column 2, the stock market data also allow us to examine sectoral heterogeneity in both the prevalence and valuation of political connections, which helps shed light on the external validity of our 27 Specifically, we focus on six salient events. (i) On February 1
- Number of household members, the adult equivalent, number of children, number of pensioners, number of female members, number of unemployed members, number of selfemployed members, number of members working in the primary sector, number of members in part-time employment, number of members with primary, secondary, and tertiary education, number of members attending an educational facility, number of foreign citizens in the household

**When combining the harmonized variables of both datasets, some significant differences between**

**both surveys become evident.** Table 3 compares the harmonized variables of both datasets to each other. The table reveals that there are some slight deviations in most of the variables and some more important ones in the case of several of the household characteristics, which is expected given that the underlying sampling designs differ from each other.<sup>19</sup> While both datasets should be representative of the underlying population in Bulgaria by design, the table demonstrates how difficult it is to achieve this in practical applications. The differences in the harmonized variables could also be related to the fact that a full harmonization between both datasets is challenging, given that some variables rely on different underlying classifications, such as in the case of the educational variable, for example. We also compare the similarity in the distribution of potential matching variables across surveys. To this end, we plot histograms and compare them to each other.<sup>20</sup> Appendix 3 presents the resulting graphs, which confirm that there are some important differences. The systematic differences in household characteristics between both datasets are an important caveat in the analysis to come.

19 First, the EU-SILC consists of a significantly larger sample than the HBS. Moreover, the time dimension of both surveys differs. In addition, the survey design of the HBS consists of strata and primary sampling units while the one of the EU-SILC consists of primary and secondary sampling units.

20 While some researchers propose additional methods, such as calculating the Hellinger Distance (HD
the analysis of data for the Islamic Republic of Iran particularly useful for an empirical illustration of Proposition 2 is the significant spatial variation observed in the Islamic 11 In practice, this assumption may seem too extreme, and welfare analysists may be reluctant to adopt it. A possible alternative, feasible under typical data constraints, would be to assume that the prices of non-food/nonrent items do not vary spatially, and that only housing expenditures are associated to the price dynamics of rent. Results obtained using this alternative approach are available in Appendix 2.

12 This is to maintain a clean connection with the theory presented in section 3. Results capture entirely and exclusively the impact of the SI and MI approaches for _spatial_ deflation.

27
is consistent with entry, but it is imprecise (t-statistic is 2.4 and randomization inference p-value is 0.239). The visual reproduction of this regression, Figure A7(a), does provide some suggestive evidence in the form of the leftmost point just above the threshold. Binding vessels just above their nearest threshold are more likely to have been built after the policy was announced than nearby vessels (other points), but the difference is not statistically significant. We explore the potential for constructing vessels just above subsidy thresholds in greater detail in Section 5.2.2.

Column 6 of Table 5 uses a different measure of entry as the dependent variable: an indicator that equals 1 if the first year we observe a vessel in the GFW data is _not_ 2016. The indicator equals 0 if 2016 is the first year we observe a vessel in the GFW data. This measure of entry differs from the previous one in that it allows already-existing vessels that were not previously observed fishing to re-enter the fishery without rebuilding their vessel. We again estimate a positive but statistically insignificant effect of fuel subsidies on entry.

Finally, we measure exit with an indicator that equals 1 if the last year we observe a vessel in the GFW data is not 2020. The indicator equals 0 if 2020 is the last year we observe a vessel in the GFW data. We find no evidence that the subsidy policy increased the exit rate from the industry: while the coefficient is slightly positive it is not statistically different from zero (Column 7 of Table 5).

# **5.2.2 Comparing vessel characteristics before and after policy**

Of the vessels in our data, 23% were built during or after 2016. Though the regulator banned the entry of new distant water fishing vessels in 2013, firms may scrap their vessels, rebuild them, and remain eligible for fuel subsidies (Ministry of Agriculture, 2013b). We compare the distance to the nearest gross tonnage threshold of vessels built before 2016 and of
or not the individual is unemployed (for migrants, the dummy represents the status before migration).

In order to proxy for the origin characteristics,<sup>31</sup> a set of economic variables aggregated at the province (and urban/rural) level is used. Since there are 64 provinces and each province consists of urban and rural areas, there are 128 sending areas in my regressions. Thus, average per capita monthly household expenditures and average per capita land areas owned by households<sup>32</sup> are computed by province (and urban/rural) from the VHLSS 2004 in order to capture the living standards and the opportunities for agricultural, forestry, and fishery activities respectively. To proxy for the chance of obtaining a job in the state sector, the proportion of state workers in total employment at each sending area is calculated from the VHLSS 2004. In terms of the proportion of workers employed by foreign firms, the number of workers in the latter sector for each location is taken from the _Enterprise Survey_ data (GSO)<sup>33</sup> and divided by the total employment.

In order to study the determinants of the migration decision for the migrants as a whole, I first run a logit model relating a dichotomous outcome of migration (migrant vs. non-migrant) to a set of personal and origin characteristics.<sup>34</sup> Table 3 demonstrates the results of logit regressions. All coefficients are reported in terms of the impacts of the variables on the odds ratios, i.e _._ , the ratios of the odds of migration occurring relative to staying. Thus, an estimated coefficient more (less) than one indicates that the independent variable is associated with a higher (lower) probability of

> 31 For the migrant sub-sample that is drawn from the VMS, information on economic conditions (such as income levels or the type of work) prior to the migration decision is not available beyond whether or not he or
is especially relevant for low-income households and microfinance borrowers whose loans are increasingly likely to be included in conventional credit bureau data, and who are less likely to be informed about the consequences of a debt moratorium on their credit score and future access to finance (de Janvry et al., 2010; de Roux, 2021). This affects not only access to finance, but may also adversely impact labor market outcomes (Bos et al., 2018).

Finally, because mandated debt forbearance is a temporary policy, an important consideration is how to optimize the time horizon of debt moratoria and how to unwind forbearance without triggering financial distress. To reduce moral hazard, banks and policymakers need to clearly communicate the extent and duration of forbearance. Because the amount of deferred debt payments is often substantial, borrowers need clarity about the repayment schedule after forbearance as well as the treatment of accrued interest. It is generally in the interest of the lender to extend the term of the loan by the number of deferred payments to avoid forcing borrowers whose incomes have not fully recovered into default.

# **3.3 Debt Restructuring**

Debt restructuring refers to a modification of a loan contract intended to reduce loan payments and avert default. Like debt forbearance, debt restructuring can be mandated by a regulator or offered voluntarily by banks and microfinance institutions. Voluntary restructuring typically occurs when a borrower’s income and ability to repay have permanently changed and temporary forbearance is not sufficient to ensure loan repayment.

Debt restructuring can modify different aspects of a debt contract. It can extend the maturity of 16
**Table 5. Tenure and Crowding in Studied Countries**

|**Per capita**|**Arm**|**enia**|**Pol**|**and**|**Rom**|**ania**|**Ru**|**ssia**|**Se**|**rbia**|
|---|---|---|---|---|---|---|---|---|---|---|
|**figures**|Space|Rooms|Space|Rooms|Space|Rooms|Space|Rooms|Space|Rooms|
|Owner-<br>occupied|10.05|0.62|28.41|1.64|16.73|1.05|15.96|n/a|24.88|0.95|
|Public rental|9.59|0.60|20.25|1.33|14.51|0.90|13.62|n/a|21.81|0.89|
|Private rental|9.23|0.58|21.40|1.27|17.92|1.04|12.57|n/a|21.47|0.81|
|Free orother|n/a|n/a|25.04|1.49|18.01|1.09|14.59|n/a|20.89|0.86|

_Source_ : Household surveys (Table 1). _Note_ : Urban apartment sector. Space is in square meters. Comparable data for Lithuania are not available.

Without detailed rental sector surveys and with inappropriate census methodologies for tracking rentals, it is hard to capture the quality effects