rafmacalaba/datause-encoder-data huggingface dataset:
18 stability is a necessary condition for unlocking the financial deepening process.<sup>10</sup> Smaller countries are less likely to be hosts to thriving financial systems as they lack the necessary scale for a diversified, competitive landscape of institutions and markets (need reference). Accessing the vast risk-pooling and diversification opportunities offered by international capital markets, while adopting appropriate macro-prudential policies to dampen the impact of potentially disruptive volatile international capital flows, can be important for such economies. Constraints imposed by market size can be partly overcome through regional integration and foreign bank entry, although risks have to be carefully managed, as evidenced by the global financial crisis. An extensive literature has shown that strengthening informational and contractual frameworks (e.g., building or upgrading of credit registries, collateral, risk insurance) and providing supporting market infrastructure can help to push out the frontier (Djankov, McLiesh and Shleifer, 2007, among others). It is important to note that these market-developing policies are long-term in nature, as they do not only involve deep and often drawn-out political processes, but their benefits also materialize over a longer horizon.

_Market-enabling_ policies help push a financial system closer to the frontier, and include more short- to medium-term policy and regulatory reforms. For instance, policies aimed at fostering greater competition can result in efficiency gains, as illustrated, by the recent vigorous expansion of profitable micro- and consumer lending across many developing countries. Such policies can also include removing regulatory impediments

> 10 For instance, deposit mobilization and credit expansion in transition economies only took off when disinflation became entrenched (IMF, 2012).
Table B2: Regression results for alternative models/estimators, Tanzania

||Naïve|reghdfe|_π_= 0|_π_= 0_._5|_π_= 1|UBH|UBS|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Child is female|0.08|0.04|0.05|0.04|0.05|0.06|0.05|
||(0.00)|(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Oldest sib|0.05|-0.13|-0.12|-0.13|-0.12|0.01|-0.14|
||(0.01)|(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Never enrolled|-0.29|0.02|-0.02|-0.06|-0.07|-0.06|-0.07|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|
|Currently enrolled|0.74|0.58|0.58|0.58|0.58|0.56|0.67|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|
|Attends private|0.28|0.10|0.10|0.10|0.11|0.11|0.09|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Never ×s�|||-0.31|-0.35|-0.25|||
||||(0.02)|(0.02)|(0.01)|||
|Private ×s�|||0.02|-0.02|-0.02|||
||||(0.02)|(0.02)|(0.01)|||
|Obs.|359,463|359,463|359,463|359,463|359,463|359,463|359,463|
|R<sup>2</sup>|0.12|0.77|0.77|0.77|0.77|0.69|0.51|

Note: columns refer to different estimators/models; UBH is the household upper bound (excludes school effects); ‘Naïve’ is simple OLS excluding fixed effects; reghdfe reports results based on the (improved, accelerated) partitioned iterative algorithm due to Correia (2017) in which household effects enter first (using the Stata command of the same name); columns π = (0, 0.5, 1) are taken from the partitioned iterative algorithm set out in the text; UBH is the household upper bound model and UBS is the school upper bound models, in which household effects are proxied by observed characteristics (not shown); all reported coefficients are significantly different from zero; cluster robust standard errors reported in parentheses.

Source: own calculations.

47
3 <!-- Start of picture text --> 12500-<br>10000<br>7500<br>7foOO -~~~~~~~~~~~~~~~~~~~~~~~~~~~~,<br>,,/<br>50001 <t,^¢ wo 0<br>0-<br>7-0 72 74 76 74k, 90 I-2 S4 t~;<br>- RXO ___.RX NO __R,N~OC<br><!-- End of picture text --> Figure 1: Turkey's exports (volume) to oil and non-oil countries; RXO : exports to oil exporting countries RXNO : exports to non-oil countries, Turkish data RXNOC: exports to non-oil countries, trading partners' data To what extent are all these wonderful export growth rates a chimera, a product of over-invoicing and straight fictitious exports? There is a widely shared view that the Turkish export boom has been at least paTtly fictitious. There Is in fact no doubt that the generosity of subsidies on in particular manufacturing exports has led many exporters to over-invoice exports. But thls is not the whole story. There has also been under-invoicing in some periods, because of a black market premium on foreign exchange. In order to capture this premium, the Turkish exporters would have to underreport their export revenues. In this section, we present an estimate of the magnitude of over and under-invoicing during the 1970-87 period. The analysis is based on an
up land markets. Then section 7 turns to the relationship between the geographic pattern of landlessness and poverty measures.

# _5.1 Incidence of rising landlessness and land transactions_

Figure 3 gives the relationship between the mean landlessness rate and log consumption per person (in 1998 prices) for 1993 and 2004. (These are non-parametric regressions, using locally-smoothed scatter plots.) Rural landlessness in 2004 tends to have a positive consumption gradient; the poorest tend to be the least likely to be landless. In terms of our theoretical analysis in section 3, this pattern is consistent with sufficiently high labor-market returns to schooling or other assets.<sup>34</sup> Note also that the landlessness rate for 2004 is nearly linear in log consumption, implying that it is concave in the level of consumption; thus higher (lower) consumption inequality at a given mean will tend to lower (raise) the aggregate landlessness rate.

How will the fact that we are using 2004 consumption (which reflects gains from the reform as well as other changes since 1993) affect the estimated relationship with landlessness in 2004? This will depend on how the consumption gains since 1993 vary with landlessness. If the (proportionate) gains tend to be higher (lower) for those who become landless and landlessness rises with consumption then we will over-estimate (underestimate) the true economic gradient in landlessness. The fact that the proportionate gain in consumption over 1993-2004 was slightly lower for those with land suggests that we will be underestimating the economic gradient in landlessness measured against pre-reform consumption.

Over the period 1993-2004, there was a reduction in landlessness among the poorest (Figure 3), though less than 3% of households in 2004 had consumption per person below the

> 34 It is not clear that returns to schooling need to be high for a positive gradient in landlessness. In common with China (Fleisher and Wang, 2004), wage compression in the more organized labor markets has kept returns to schooling relatively low in Vietnam; see Gallup (2004) and Nguyen (2004b) for the 1990s, although it appears that returns to education have increased substantially in recent years (World Bank, 2005, Chapter 7). Note also that the gradient could reflect a wealth effect on non-farm earnings.

22
distortion. Consistent with the “evergreening” hypothesis, we find evidence of a shift in post-program bank lending away from districts with greater bailout exposure.

Table VI implements the specification in equation 4.5, which uses bank-district level data and controls for district-time fixed effects to verify that the results from our preferred specification are not driven by changes in credit demand. As in Table IV, column [1] uses the log number of accounts as the dependent variable, and column [2] uses log credit as the outcome of interest. To allow for a meaningful comparison, we restrict the sample in Table VI to the districts included in our estimation of equation 4.1. We find that banks experiencing a larger equity shock as a result of their bailout increase their overall lending, but reallocate credit away from districts with greater exposure to the bailout. The point estimates are negative and significant for both the number of loans (ˆ _γB_ =-0 _._ 210, s.e.=0 _._ 033) and the amount of credit (ˆ _γB_ =-0 _._ 142, s.e.=0 _._ 045), and the size of these effect is even larger than in the district-level estimates reported in Table IV. Taken together, these results indicate a strong supply-side response that is not confounded by changes in credit demand.

# **4.2 Impact on Loan Performance and Moral Hazard**

We next examine the effect of the bailout on ex-post loan performance. Table V presents the results, using the basic specification described in equation 4.1. The dependent variable columns [1] to [4] is a dummy equal to one if the branches in a given district experienced an increase in the share of non-performing loans between year _t −_ 1 and _t_ , while the dependent variable in columns [5] to [8] is a dummy equal to one if bank branches located in a given district experienced an increase in the share of non-performing credit over the same time period. As before, our preferred specification includes a full set of fixed effects, time-varying controls and linear pre-trends for each district (columns [4] and [8]).

We find evidence of a strong negative effect of the bailout on loan performance. The 20
Figure 1: Graduation Rates and Learning Outcomes <!-- Start of picture text --> 70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2008 2009 2010 2011 2012 2013 2014<br>On-time graduation Enlace (Spanish) Enlace (Mathematics)<br>performance (%)<br>On-time graduation (%)<br>Share of takers with good or excellent<br><!-- End of picture text --> The EMS system is characterized by strict promotion criteria. Students must pass five of eight disciplinary subject areas and practical modules. Otherwise they have to repeat the semester. Students who fail three or fewer subject areas can enroll in the next semester, but they have to attend and pass intensive courses (the so-called _regularizacion_ ) during a fixed time window. In addition, students must satisfactorily complete all their subject areas and modules within 10 semesters after enrolling in EMS, otherwise they lose the right to re-enroll. Partly as a result of the strict promotion rules, there are very high grade and subject repetition rates, 15.3 percent and 31.3 percent, respectively in 2013.<sup>3</sup>

# **2.2 The PROBEMS Scholarship Program**

In the context of a major EMS curricular reform<sup>4</sup> , with the aim of reducing school dropouts and improving learning outcomes, in 2007 the Secretariat of Public Education (SEP for its acronym in Spanish) introduced a new scholarship program, PROBEMS. The program targeted poor EMS students mostly in urban areas since, at the time, a national conditional cash transfer program, _Progresa_ / _Oportunidades_ later renamed as _Prospera_ , already benefited poor EMS students in rural areas.<sup>5</sup> The number of beneficiaries under

> 3Students who fail three or more subjects for two consecutive semesters have to repeat the entire grade.

> 4For more information on the EMS reform of 2008, see SEP (2008): http://cosdac.sems.gob.mx/portal/index.php/riems.

> 5The conditional cash transfer program has since then expanded to many urban areas.

6
# **3 TUP Program**

Originally designed by BRAC in Bangladesh, the TUP program targets UP women and provides them with a productive asset (typically livestock), training, mentoring, a cash stipend, and other services for a set period until the household “graduates” into a more sustainable, self-sufficient livelihood.

The TUP program was implemented in eight Afghan provinces under the World Bank-supported Access to Finance program between 2015 and 2021, reaching 12,698 households. The impact evaluation focuses on Balkh province, where approximately 1,500 households were reached. The intervention was implemented by the Microfinance Investment Support Facility for Afghanistan (MISFA), an independent apex organization. Various local nongovernmental organizations (NGOs) delivered the frontline program activities in collaboration with MISFA.

Program villages were selected among the poorest villages in the province. A participatory rural appraisal (PRA), through which the community ranked the population of each village based on their socioeconomic status, was conducted to identify UP households. This was followed by a verification survey to ensure that households met program eligibility criteria.

While similar in approach to other TUP programs, the exact design for the Afghanistan TUP was developed by MISFA to address local constraints, and its duration was shorter than other TUP programs implemented before (12 months vs. 18-24 months). The following components were delivered to the eligible households:<sup>10</sup>

1. Productive asset transfer (although households could choose from several types of livestock, TUP households mainly selected a cow with a calf or a pregnant cow; livestock was replaced if livestock became sick or died during the program duration).

2. A monthly cash transfer (USD 15 nominal per month for 12 months).

3. Biweekly training sessions on livestock rearing and entrepreneurship for 12 months.

4. A health subsidy with a hygiene kit worth about USD 42 nominal.

5. Fortnightly mentoring visits by social organizers to discuss topics related to health, education, women’s empowerment, financial inclusion, and social cohesion/community support.

6. Veterinary services to evaluate livestock assets and provide additional support if necessary (food supplements or asset replacement).

7. Linking households to education, health, and financial institutions where needed.

> 10For a more detailed program description, see Bedoya et al. (2019).

10
# V. Descriptive Analysis – Poverty exposure bias for temperatures

To investigate the exposure of poor and non‐poor households to high temperatures within the 52 countries we define a “poverty exposure bias” (PEB) that measures the fraction of poor people exposed, compared to the fraction of all people exposed per country. When estimating the number of people exposed (and not exposed), we incorporate DHS data on household size and also use household weights to ensure the representativeness of our results. We compute the PEB as follows:

## Poverty Exposure Bias� �<sup>Share of poorest quintile exposed</sup> �1 Share of all quintiles exposed

If the PEB is greater than 0, poor people are more exposed to high temperature stress than average; if the PEB is less than zero, poor people are less exposed. For instance, if within a country 25% of the population in quintile 1 are exposed to high temperatures and 20% of the entire population is exposed, the PEB would be 0.25. Since the wealth index is comparable only within and not between countries, the PEB we calculate in this paper is an estimate of whether poor people are more or less exposed compared to the entire population _within_ a specific country.

This PEB metric has recently been applied to floods and droughts (Winsemius et al., 2015). While for floods defining exposure is fairly straightforward (either a household is in a flood zone or not), for droughts and temperatures, defining exposure is not as direct. Here we define the exposure to heat waves in a way that is comparable with the approach followed for floods and droughts in Winsemius et al. (2015).

First, we obtain the maximum monthly temperature from CRU from 1960‐2013 for each household in the sample (all of the 690,000+ households). This provides us a distribution of extreme monthly temperatures for each household, which is used to assess the risk of high temperature. With this distribution, we then calculate the number of months a household experienced a temperature above the 98<sup>th</sup> percentile of country‐month temperatures in its countries. For instance, if the 98<sup>th</sup> percentile of temperature in Nigeria over the considered period (1960‐2013) is 30 degrees C, and household in Nigeria experiences a temperature of 31 degrees C, we mark that household as experiencing a “hot” month. Across the sample of 1960 to 2013, we calculate the number of “hot” months each household experiences.

If the total number of “hot” months a household experiences is more than 2% of all months from 1960‐2013 (that is, if the household experiences more “hot” months than the average in the country), then that household is classified as being _over‐exposed_ to high temperatures. If the household experiences less “hot” months than is expected, that household is classified as _under‐ exposed_ .

15
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25
river basin management, etc. The dialogue strategy has also been used to help solve some social conflicts at the community level such as urban construction resettlements. Mass media at all levels in China have widely reported the dialogue programs. A few cases are presented below in order to further illustrate the approach<sup>9</sup> .

# **4.4. Cases**

# **_Case 1: Urban Solid Wastes (Wanshen City, Chongqing Municipality)_**<sup>**_10_**</sup>

Wanshen City, a district of Chongqing Municipality, which is located at the southeast of Chongqing City, is an urban area developed from mining industries, with a land area of 566 square kilometers and a population of about 0.3 million. Most of the residents have some relations with the mining industries. Due to the privatization of the mining industries and the reform of social responsibilities of corporations, social and environmental issues in Wanshen had been enormous. Complaints and conflicts between the residents, the government agencies and the industries were getting more and more serious before 2005.

In 2004, the author introduced the stakeholder dialogue experiments that the author was doing in China to one of the high-level officials of Chongqing municipal government, and consequently a joint effort in designing and implementing stakeholder roundtable dialogue in Chongqing was launched by the World Bank and Chongqing Municipal Government in 2005, where Wanshen was selected as one of the districts piloting the dialogue approach.

Among those first roundtable dialogues conducted in Wanshen was the dialogue on garbage collection in Heping Village of Donglin District on March 15, 2006. The key environmental issue was that the garbage was scattered everywhere in the streets due to insufficient number of garbage cans. The issue was simple, but to find a solution had been a complicated process. The first difficulty was that there were no clearly defined agencies that could be held responsible for the issue. Many government units and corporate entities should take some responsibility, but all of them had some excuses to take no actions. After about 2 hours of discussion, two units – one in the government sector and one school located within the village, volunteered to take actions. While it was too expensive to distribute rubber garbage cans in the streets as used in richer areas, it was decided in the roundtable meeting to use bricks and cement to build garbage collection cans which are much cheaper.

> 9 See CECE (2009) for more cases and implementation guidelines.

> 10 Source: Chongqing project report and internet reports.

10