rafmacalaba/datause-encoder-data huggingface dataset:
tionally, certain aspects of forcibly displaced populations—including a disproportionate share of women and children whose access to health care was already precarious before migration—may produce diverse effects from those in the Global North.

We focus on the Permiso Especial de Permanencia (PEP), a regularization program that Colombia offered in 2018 to approximately half a million undocumented Venezuelan migrants there. PEP beneficiaries received work authorization and full access to social services for up to two years.<sup>2</sup> We examine how PEP impacted household fertility by leveraging information from two waves of the Venezuelan Refugee Panel Study (VenRePS), a representative survey of undocumented Venezuelan migrants who were living in main urban centers in Colombia before PEP.<sup>3</sup> Approximately half of the households in the survey were eligible for the PEP program.

Using panel data on 1,346 households, we compare the probability of having young children (conceived after the program launched) among households that were eligible and ineligible for PEP before and after the program began. Specifically, we observe each household at three points in time: at baseline, two, and three years after PEP’s rollout. Our models include household-survey and wave fixed effects to account for unobserved time-varying factors that potentially shaped household fertility. In addition, they incorporate a rich set of municipality baseline covariates interacted with time trends to address non-parametric changes in city-wide characteristics affecting childbearing choices.

We find consistent and robust evidence that the PEP program decreased childbearing likelihood among migrants. Based on our main estimates, migrant households eligible for PEP were 3.9 percentage points (pp) less likely to have children less than one year old, 7 pp less likely to have one-year-olds, and 1.8 pp less likely to have two-year-olds. Falsification tests confirm the lack of changes in the probability of having children conceived

> 2PEP was followed in 2021 by the opportunity, via a separate program, to enjoy the same benefits for an additional 10 years.

> 3Bogot ́a, Medell ́ın, Barranquilla, and a fourth group of smaller cities.

3
that gap, exploring two questions related to household income composition and welfare outcomes in this new and unprecedented context. We take advantage of rich survey data to assess trends in income composition over time and to understand the relationship between livelihood diversification and household welfare outcomes, in particular, food security. As our data include a pre-outbreak baseline, we are able to observe households’ status prior to the pandemic and then track them through and beyond the first year of the pandemic. This panel data, along with our empirical strategies, allows us to identify causal relationships between a household’s choice of livelihood activities, income sources, and their level of food insecurity during the pandemic.

In terms of how household livelihood diversification has changed since the onset of the pandemic, we do not observe substantial or systematic changes in household income composition. Small differences exist in Ethiopia and Malawi, where households became more specialized during the pandemic than before. Conversely, in Nigeria diversification increased slightly after the start of the pandemic. From these trends we conclude that households made limited use of livelihood diversification as a coping strategy during the pandemic.

In terms of how household income composition impacts food insecurity, our regressions provide little evidence to support the idea that livelihood diversification improved food insecurity during the pandemic. The preponderance of evidence, across countries, estimation methods, and measures of diversification is that there is no significant relationship between livelihood diversification and food insecurity. We provide a number of robustness checks and evidence that we believe demonstrates that the null results are true nulls. Though income diversification may serve as an effective _ex ante_ or _ex post_ coping mechanism for many shocks, in particular small transitory shocks, we find that for a disaster on the scale of the COVID-19 pandemic, this strategy does not appear to be effective.

An optimistic interpretation of the evidence in this paper would be that the extreme socioeconomic impacts of the pandemic appear to necessitate alternative adaptation strategies. A pessimistic interpretation is that a pandemic is too disastrous and omnipresent to prepare for or adequately adapt to. Either interpretation leads to the conclusion that commonly promoted coping strategies, such as livelihood diversification, that households are encouraged to undertake on their own are inadequate for large-scale or long-term disruptions and disasters. As households, development agencies, and governments look to prepare for the increased occurrence of such disasters, either due to climate change or the increased spread of zoonotic disease, this point bears keeping in mind. Future research and development will need to grapple with the fact that the coping strategies that gave people hope in the past may fail them as they try to cope with the increased scale of shocks in the future.

18
**<u>Table 2: Regional HHI for Electricity Generation (2003)</u>**

||**GLOBAL**|**Africa**|**Asia &**<br>**Oceania**|**Latin**<br>**America**|**North**<br>**America**|**Eastern**<br>**Europe**|**Western**<br>**Europe**|
|---|---|---|---|---|---|---|---|
|**Market indicator**||In|stalled Capa|city percountr|y (MW)|||
|**HH min**|**734**|592|594|1762|20|48|1386|
|**HH max**|**1630**|1527|1261|3467|206|416|2893|
|**Aggregate market**<br>**share**|**18%**|16%|15%|47%|10%|8%|40%|
|**Number of firms**|**16**|5|10|10|9|7|12|
|**Number of**<br>**countries**|**57**|6|14|15|2|8|12|
|**Cases**|**157**|7|29|60|12|13|36|
|**Main actors**|**AEP, AES, CLP,**<br>**Duke, E.ON, EDF,**<br>**EDP, ENDESA,**<br>**ENTERGY, UF,**<br>**HEG, Intergen,**<br>**PSEG, Reliant,**<br>**Southern, Tractabel**|AES,<br>EDF,<br>UF,<br>Intergen,<br>PSEG|AES,<br>CLP,<br>Duke,<br>EON, EDF<br>HEG, UF,<br>Intergen,<br>PSEG,<br>Tractebel|AEP, AES,<br>Duke, EDF,<br>EDP,<br>ENDESA,<br>UF, Intergen,<br>PSEG,<br>Tractabel|AEP, AES,<br>Duke, E.ON,<br>ENTERGY,<br>PSEG,<br>Reliant,<br>Southest,<br>Tractabel|AES,<br>EDF,<br>Entergy<br>E.ON,<br>PSEG,<br>Tractebel,<br>UF|AEP, AES,<br>Duke, E.ON,<br>EDF, EDP,<br>ENDESA,<br>UF, Intergen,<br>PSEG,<br>Reliant,<br>Tractabel|

At the regional level, a strict adherence to the rule suggested above would however only clear North America and Eastern Europe of any market power. Both Latin America and Western Europe should be sources of concern for an hypothetical supranational competition agency as indicated by the HHIs and hinted at by the large market shares of the operators present in the regions. For Western Europe, this is not a surprise since the liberalization of the sector has not yet fully been implemented. For Latin America, this comes as a major surprise. Indeed, Latin America is the region in which the largest number of countries have some significant reform levels in the sector. In over half of the countries of the region, the private sector now has a significant role in generation. Yet Table 2 suggests that even the lower bound of the HHI is close to what would justify a pro-active role for a supranational competition agency concerned with the degree of concentration in the sector.

# b. **Electricity Distribution**

For distribution, we have data on 12 firms present in 31 countries (12 OECD and 19 non OECD). They account for 31% of the global electricity distribution market. We only have data on one of the two countries for North America and hence we do not deal with it at the regional level since the methodology would produce a country specific indicator. The sample size for some of the regions (Africa and Asia) is also rather small—to a large extent because of the few deals with private operators in this activity in both regions--and hence the results are likely to be biased and of lesser interest. The companies included are: American Electric Power, AES Corporation, CLP Group, E.ON, Electricité de France (EDF), Electricidade de 11
**Table 16: Interaction with Firm Size, Georgia**

||Percentage<br>perm full-ti<br>since De|change of<br>me workers<br>c 2019|Decreased tot<br>temp wo|number of<br>rkers|Decreased to<br>operat|tal hours of<br>ions|Anticipate<br>arrears on<br>liabi|falling in<br>outstanding<br>lities|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Received national or local|0.665**|0.504**|-1.037*|-0.934|-0.451|-0.911|-0.633|-0.258|
|government support|(0.275)|(0.223)|(0.543)|(0.676)|(1.080)|(1.050)|(0.798)|(0.804)|
|Support X ln of employment size at<br>ES|-0.196*|-0.153**|0.296**|0.311**|0.175|0.166|0.051|0.018|
||(0.100)|(0.066)|(0.132)|(0.138)|(0.272)|(0.269)|(0.253)|(0.253)|
|Average change in monthly sales<br>compared to one year ago|0.117|0.093|-1.169*|-1.579**|-2.652***|-2.973***|-2.972***|-2.570***|
||(0.169)|(0.193)|(0.638)|(0.707)|(0.630)|(0.717)|(0.620)|(0.733)|
|Decreased demand for products<br>or services compar to one year|-0.028|0.090|0.775**|0.855|1.493**|0.961|1.248*|1.546*|
|ago|(0.078)|(0.088)|(0.352)|(0.611)|(0.638)|(0.627)|(0.645)|(0.877)|
|Decreased supply of inputs<br>compared to one year ago|-0.197**|-0.396***|0.018|0.295|1.118***|1.223***|-0.717|-0.697|
||(0.094)|(0.150)|(0.334)|(0.378)|(0.378)|(0.377)|(0.441)|(0.528)|
|Decreased liqdt or cash flow<br>avail. since COVID-19 began|-0.104<br>(0.086)|-0.307*<br>(0.171)|-0.142<br>(0.568)|0.352<br>(0.607)|-0.504<br>(0.350)|-0.451<br>(0.423)|0.847<br>(0.750)|0.453<br>(0.902)|
|Overdue on obligations to<br>financial institutions|-0.075|0.052|0.713|0.661|0.171|0.525|1.534***|1.044|
||(0.052)|(0.100)|(0.506)|(0.550)|(0.514)|(0.357)|(0.455)|(0.636)|
|Ever temporarily closed during<br>the COVID-19 outbreak|-0.597***|-0.388|0.103|-0.306|0.379|0.467*|-0.132|-0.098|
||(0.093)|(0.259)|(0.361)|(0.349)|(0.305)|(0.271)|(0.437)|(0.551)|

54
to all respondents at the beginning of enumeration. We refer to the remainder of variables, which amount to 37 items in our Indonesian dataset, as optional. In the subsequent discussion and simulations, we take the core questions as given and refer to optional questions when discussing items such as questionnaire length.

Having been selected for administrative reasons, the short core questionnaire has limited predictive power for household consumption. At the other extreme, a full socio-economic population sample survey contains rich household information that confers greater predictive accuracy, but is unsuitable for administration to a large section of the population. The following three subsections describe methods that provide a menu of choices in between, and expose the relationship between survey cost and accuracy.

# **3.1 Truncated questionnaires**

A simple approach is to order the optional features from most to least predictive. A truncated questionnaire, i.e. one that is limited to a certain number of questions, arises from adding the desired number of most predictive features to the core set. Computation of the predictive accuracy for each of the iteratively growing variable sets provides the cost-accuracy trade-off. In addition to a predictive model, this approach requires an ordering of features by their predictive power for household consumption. We test several variable selection methods to perform such an ordering and thus generate a sequence that can be truncated.

- _Stepwise selection._ The classic mechanism of PMT modelling<sup>6</sup> . To generate a complete variable sequence instead of a particular set of regressors, we adjust algorithm 1 by eliminating the stopping criterion and recording the order in which variables are added to the set of predictors.

- _Variable importance._ Tree-based predictive models perform variable selection during the construction of their tree structure. Our gradient boosting model, which is based on decision trees, generates two measures of variable importance. One is the proportion of trees in which each variable is used, the other is the sum of increases in the trees’ objective function produced by the splits of each variable. We translate both the split and gain measures into two separate variable orderings. The first is to rank variables in descending order of either the number of splits, or of the gain they produce, when the full set of variables is considered jointly. The second uses a backward selection approach, recursively eliminating the variable with the lowest split or gain, respectively.

- 6Alternative methods have begun to gain traction among practitioners [Grosh et al.,

- 2022].

15
# **Sampling technique**

To understand the representativeness of a sample, it is very important to be aware of the sampling technique used. Selection bias happens when the selected participants are not

**_Selection bias occurs when the selected participants are not representative of the priority population._**

representative of the entire population to be studied. In the probability method, all individuals in a population group have the same opportunity of being selected, thereby obtaining representative samples of the priority population. By contrast, in a nonprobability sampling such as convenience sampling, each individual has a chance of getting selected based on a set of determined characteristics, which leads to less representative samples. The sampling method used by studies of hidden or hard-to-reach populations (for example, injection drug users or men who have sex with men) deserves special consideration. Because of the **_In the probability method, all_** discrimination and stigma often associated with **_individuals in a population group have_** these population groups, it is practically **_the same opportunity of being_** impossible to have a sampling framework that **_selected._** would accurately define the sample universe from which individuals are randomly selected. One example of a sampling framework for a general population is the telephone directory. However, this framework would be useful only if all inhabitants have a fixed telephone land line and each telephone number is listed in the directory. Otherwise, a selection bias would again be in play. For most hidden populations such a sampling framework cannot be established.

Thus, a strategy used in many studies is to obtain convenience sampling in places where members of the key population group frequently gather, such as bars or clubs. Obviously, individuals who frequent such places are the ones with a higher possibility of getting selected, a situation that does not comply with the principle of “equal likelihood of being selected for all individuals in a population group”—the definition of the probability

**_Alternative sampling techniques for use on hidden populations have been developed._**

sampling method. Such an approach also limits the possibility of making generalizations. In response to this constraint and based on a CDC initiative (Centers for Disease Control in Atlanta, Georgia USA), the implementation of other sampling techniques that provide an alternative to the traditional probability/nonprobability approach have been studied over the past few years<sup>(15, 16)</sup> . These alternative methods are RDS (Respondent-Driven Sampling) and TSS (Time-Space Sampling). The data quality analysis tool considers the use of probability sampling, or the alternatives RDS and TSS in hidden populations, a satisfactory sampling technique. Given the importance of this methodological characteristic, this criterion has been weighted and its result multiplied by a factor of two.

15
# **Executive Summary**

**Deep and liquid government securities markets are critical for effective fiscal and monetary policies and are the cornerstone of financial market development.** These markets support sustainable growth by ensuring stable government financing under different economic cycles and by broadening the scope of instruments and channels for the effective implementation of monetary policy. They also provide a benchmark and the institutional infrastructure for broader capital market development and management of financial risks, enhancing the resilience of the economy to adverse shocks.<sup>1</sup> Local currency bond markets have grown considerably in emerging market economies (EM), but remain relatively undeveloped in MENA. Consolidation of EM local currency debt as an asset class is widespread in other regions.

**The paper focuses on five countries in the non-GCC MENA region that have government bond markets with a minimum size and greater potential for market development: Egypt, Jordan, Lebanon, Morocco and Tunisia (MENA-5).** These countries have sizable debt-to-GDP ratios and domestic tradable debt and have, to different degrees, implemented measures to develop their local debt markets. They are all middle-income, oil-importing countries with diversified exports. These macroeconomic characteristics help explain their greater need to issue debt and rely on domestic debt markets.

**MENA debt markets are less developed and underrepresented in global bond indices compared to other emerging regions.** For example, the only MENA countries included in global EM local currency bond indices are Egypt (in J.P. Morgan’s GBI-EM and Markit’s GEMX) and Morocco (only in GEMX). Their weights are among the bottom quartile of all countries, and MENA accounts for only 2.1 percent of GEMX and 0.2 percent of GBI-EM Broad Diversified. Inclusion in these indices requires minimum investability of a country’s debt market, which closely relates to key building blocks for market development. GEMX, for example, takes into account capital controls, taxes, secondary market liquidity, size of the investor base, quality of regulations and market infrastructure.<sup>2</sup> These criteria were applied to 33 EMEs including Egypt, Morocco, Tunisia and Lebanon. They received low scores, ranking respectively 21<sup>st</sup> , 29<sup>th</sup> , 30<sup>th</sup> and 33<sup>rd</sup> . Tunisia and Lebanon did not reach minimum scores for inclusion in the GEMX.

## **_Overview of bottlenecks in public debt market building blocks_**

**Country differences notwithstanding, the underdevelopment and low representation of MENA markets are due to several common features in the five key building blocks that sustain deep and liquid public debt markets:**

> 1 For more details see World Bank and IMF (2001): ”Developing Government Bond Markets: a Handbook” and Arvai and Heenan (2008): “A Framework for Developing Secondary Markets for Government Securities.” IMF WP 08/174.

> 2 For more details see Crisil (2009) “Gemloc Investability Indicators — Phase 2 extension.”

3
-50--

# FIGURE 6b. Financial Deepening artd the Real Interest Rate In Indonesia

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achievement of low and stable inflation. Because high inflation tends to be volatile inflation, it has the effect of making real interest rates both negative as well as unpredictable, tending to increase the risk associated with financial saving and thus discouraging such saving. Policies toward the financial sector included the achievement of financial sector stability, resisting severe financial repression, and making formal saving vehicles more convenient to small savers. The governments of high-performing Asian countries were credited with achieving the security of banks by protecting them from competition (mainly through restrictions on entry, except in Hong Kong, Indonesia, and Singapore), and safer banks encourage saving by reducing the risk associated with financial saving. In contrast with other literature of the time, the elimination of financial repression is not cited in the Bank study as a reason for higher saving rates, because repression was retained for most of the high growth period in the high-performing Asian economies. However, these countries practiced a relatively mild form of financial repression, which was consistent with positive real interest rates and financial deepening. In addition, a reduction in the degree of financial repression practiced in the region is cited as possibly having contributed to an increase in saving rates over time. Finally, policies to promote the accessibility of the formal financial sector during the high-growth period included bank branching to rural areas and the adoption of postal saving schemes. Postal saving systems in Japan, Korea, Malaysia, Singapore and Taiwan, for example, were designed to attract small savers (by offering greater security and lower transaction costs than the informal financial sector). However, much of this reasoning seems more relevant for savers<sup>’</sup> decisions regarding the allocation of their assets between the domestic financial system and other alternatives, than for the aggregate saving flow.

Other explanations focus on less orthodox factors. Some authors, for ex-ample, have emphasized the roles of restrictions on consumer credit and forced saving schemes in explaining high Asian saving rates. Restrictions on consumer credit in the context of financial repression, coupled with taxes on luxury goods, are perceived to have been important in encouraging household saving. The financial systems in countries such as Japan, Korea and Taiwan channeled resources into infrastructure and productive business uses, and away from housing and consumer credit which, according to some authors (see Patrick 1994), may have resulted in higher saving rates and more rapid growth, but at some welfare cost. In turn, the implementation of such provident funds in some countries (Japan, Singapore and Malaysia) may also have made 26