rafmacalaba/datause-encoder-data huggingface dataset:
17

**Table 6: Demand and Supply of Private Credit (Disequilibrium estimation)**

|Equations|Estimates|
|---|---|
|**Supply equation**||
|Constant|6.6444***|
||(7.2737)|
|Real lending rate|2.7062***<br>(4.5826)|
|Real T-bill rates|-2.1912***<br>(-3.8632)|
|Ln(Lagged lending capacity)|0.1387*<br>(1.2887)|
|Variance of residuals|0.0564***|
||(48.308)|
|**Demand equation**||
|Constant|4.7733***|
||(9.5659)|
|Real lending rate|0.7455<br>(1.1246)|
|Ln(Lagged real industrial production)|0.6378***<br>(9.7747)|
|Ln(Lagged stock market index)|-0.1127***<br>(-8.4481)|
|Variance of residuals|0.0066***|
||(187.20)|
|Log-likelihood|-84.0863|
|Adjusted R2|0.4750|
|Frequency of supply regimes (FS)|22.22%|
|Frequency of demand regimes (FD)|77.78%|
|Akaïke Information criteria|-252.41|
|Schwarz Information Criteria|-234.34|

Notes: 1) Asymptotic t-statistics are in parentheses.

2) FD (FS) denotes the frequency of demand (supply) regimes given that a period or regime occurs when the corresponding probability is higher than 0.5.
**<u>Table B2. SCM Detailed Results for All IHC Firms - Clearance Time at 90</u>**<sup>**th**</sup> **<u>percentile</u>**

|IHC firm #|6-month average<br>treatment<br>effect:change in<br>log of clearance<br>time at 90<sup>**th**</sup><br>percentile of<br>firm-month<br>distribution<br>(�_���_��<br>�<br>�<sup>)</sup>|Number of donor<br>firms used to<br>construct<br>synthetic firm|Rank of IHC firm relative<br>to 25 placebo firms|Percentile<br>rank statistic<br>(rank/26)|
|---|---|---|---|---|
|1|0.189|10|18|0.692|
|2|-1.613|10|1|0.038|
|3|-0.125|9|11|0.423|
|4|-0.362|13|10|0.385|
|5|-0.122|9|12|0.462|
|6|0.262|6|18|0.692|
|7|-0.580|8|10|0.385|
|8|-2.088|6|1|0.038|
|9|-0.377|9|5|0.192|
|10|-0.048|5|12|0.462|
|11|0.918|31|25|0.962|
|12|-1.107|8|4|0.154|
|13|-0.845|8|3|0.115|
|14|-0.682|20|4|0.154|
|15|0.513|50|21|0.808|
|16|0.023|6|15|0.577|
|17|0.334|9|18|0.692|
|18|-0.802|9|2|0.077|
|19|-0.762|5|4|0.154|
|20|-0.290|6|8|0.308|
|21|-1.041|8|4|0.154|
|Mean % change in 90<sup>th</sup><br>pct clearance time|-0.410||Mean percentile rank<br>statistic(�)|0.377|
|Median % change in<br>|-0.362||||
|90<sup>th</sup> pct clearance time|||||

Notes: Synthetic control firms were constructed with the benchmark specification that includes three pre-treatment values of the outcome variable as part of the set of conditioning variables. The summary statistics at the bottom left of the table are obtained as the mean and the median of the treatment effect across the 21 firms. The statistic at the bottom right of the table �̅ given 21 firms has 5% critical values of 0.377 and 0.623 and 1% critical values of 0.339 and 0.660 (based on Appendix table A1 in Dube and Zipperer (2015)).

49
These allocation principles are, however, purely theoretical and it is difficult to know what prevails in actuality. For example, should groups get organized to capture and retain control of part of the assistance, then those principles can hardly be operational. However, if policymakers are able to identify the people’s needs, keeping anonimity functional, then they will be better equipped to devise and then implement and operate pre-established aid-allocating rules and principles and thus target those who need it the most.

In order to assess the impact of aid upon food security and to determine aid allocation conditions after the disaster in Haiti, we present a simple empirical model.

with where FCSit is the food consumption score of household i at time t, EAit is the emergency assistance variable which values 1 if the household gets assistance and 0 otherwise, Ait-1 is a preearthquake assets index, whereas Ait is a post-earthquake assets index, Lit is a measure of assets losses, Hit is a variable of housing damage and Xit is a vector of household characteristics.

In this paper, the pre-/post- earthquake analysis of food security and emergency assistance allocation makes it possible to test different hypotheses concerning issues of assistance targeting efforts.

First, we consider the possibility that the targeting of assistance programs is based on the pre-disaster level of assets and asset losses, that is  < 0 and  > 0. Second, we can suppose and test whether, in absolute value, the weight placed on pre-disaster assets is greater than the weight placed on asset losses, that is |  | <  . Indeed, while satisfaction depends on what a household possesses, deprivation depends on what a household does not have yet believes they could or should have, that is on the extent of their losses (Morris and Wodon, 2003). Finally, we also want to test whether housing damage has an impact on assistance allocation, that is  > 0.

8
interviews: the typical Rwandan dwelling is quite dark even during the daytime and people sometimes use artificial lighting in their homes. To the extent the Pico‐PV lamp replaces a traditional lighting source for their daytime housework activity, lighting quality clearly improves. Yet, people might also relocate outside activities indoors and replace natural daylight by the Pico‐PV lamp. In this case, lighting quality would probably not improve, but still it demonstrates the higher flexibility people have in organizing their daily tasks.

**Table 8. Daily Time Awake, Time Spent on Domestic Labor and Any Income Generating Activity**

|||Treatment|Control|_ITT_|_p_‐value|
|---|---|---|---|---|---|
|Time awake||||||
||Head of household|14h28|14h27|_0h05_|.739|
||Spouse|14h46|14h36|_0h11_|.378|
|Domestic labor||||||
||Head of household, total|2h08|2h10|_‐0h01_|.950|
||Head of household, after nightfall|0h16|0h12|_0h04_|.542|
||Spouse, total|2h48|2h30|_0h16_|.333|
||Spouse, after nightfall|0h32|0h31|_0h02_|.779|
|Any income|generating activity of subsistence|||||
|farmers||||||
||Head of household, total|5h37|5h29|_0h21_|.215|
||Head of household, after nightfall|0h01|0h01|_0h00_|.823|
||Spouse, total|5h37|5h25|_0h10_|.354|
||Spouse, after nightfall|0h00|0h01|_0h00_|.462|

_Note:_ The ITT depicts the difference in means at the follow‐up stage between the whole treatment and control group,
off farm in September 2009 had not found a job by April 2009 (row 1). These are the long-term laid-offs in our sample. By August 2009, when we re-contacted these individuals, 30 percent more of them had found a job off farm (row 2). This means that the contribution to the rural unemployment rate due to the financial crisis was down to only 1.5 percent to 2 percent (about 8 to 10 million individuals). Clearly, the scare of potential instability driven by rural unemployment posed for China by the Global Financial Crisis was largely dampened by 11 months after the onset of the crisis. This study’s findings of continued recovery in employment are supported by data reported by the China National Bureau of Statistics (CNBS, 2010).

The data suggest that the profile of those who could not find a job by August 2009 is close to that of individuals who had never left the village—female, older and uneducated (see de Brauw et al., 2008). While 33 percent of male long-term laid-offs found a job between April 2009 and August 2009, only 25 percent of women did (rows 3 and 4). The difference between those of different age and education groups were even larger (rows 5 to 10). While 43 percent of those under 30 had found an off-farm job by August 2009, only 17 percent of those over 50 had. Forty-five percent of long-term laidoffs with more than a high school education had found a job. In contrast, only 23 percent with less than a middle school education had found off-farm employment.

# **6. Conclusion**

The immediate shock to rural off-farm employment that occurred with the onset of the global financial crisis was large. More than 49 million rural workers lost their jobs. However, the size of this shock is unsurprising. As a producer of consumer goods for the 22
Chinese Center for Disease Control and Prevention,” JAMA. February 24. JAMA. 2020;323(13):1239-1242. doi:10.1001/jama.2020.2648 Wu, J. and others (2020a). “Estimating Clinical Severity Of COVID-19 From the Transmission Dyanmics In Wuhann, China,” Nature Medicine. March Wu, J. and others (2020b). “46,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak,” The New York Times. April 30.

https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html Yang, J. and others (2020). “Prevalence of Comorbidities and Its Effects in Patients Infected With SARS-Cov-2: A Systematic Review and Meta-Analysis,” International Journal of Infectious Diseases. March.

28
In the context of developing countries, there is widespread concern that if the MPAs established do not benefit coastal communities, increased degradation and a decrease in the services provided by the existing coastal ecosystems may ultimately occur, leading to greater poverty. Hence, conservation organizations supporting MPAs in Tanzania, such as WWF and IUCN, are trying to address poverty in their conservation programs (Ireland, 2004). IUCN (2003) states that “it is unacceptable to carry out conservation activities in areas of high or endemic poverty while turning a blind eye to the needs of the poor people who live there and depend on the same biological resources that are often those that we wish to conserve.” There is also growing recognition that conservation is unlikely to be effective if users of resources are excluded from the decision making process (Ireland, 2004). However, some have argued that when conservation programs have become too diluted by strategies to promote community development and greater local participation in decision making, conservation objectives may suffer (Kramer and others, 1997; Brandon and others, 1998, Oates, 1999). How to balance the MPA multiple objectives of conservation and development is therefore a topic of increasing relevance for countries such as Tanzania.

This paper seeks to contribute to this literature by investigating the linkages between household characteristics, MPA management activities, and household choice of fishing gear. We first look at which household characteristics determine the use of destructive fishing gear. In particular, are poor households more likely to use destructive fishing gear? And does the use of destructive fishing gear increase household welfare? Second, we seek to evaluate the impact of MPAs on the use of destructive fishing gear and household welfare. Do MPAs in Tanzania, through their efforts in enforcement of fishing gear regulations, education and awareness campaigns on the impacts of destructive fishing gear, fishing gear exchange programs, and provision of alternative income generating activities (AIGA), have an influence on household use of destructive fishing gear? Do MPA restrictions on access negatively impact households dependent on fishing? These are the questions this paper seeks to answer.

The empirical analysis is based on purposely collected household survey data from a sample of villages located along the coastal areas of mainland Tanzania and Zanzibar.<sup>3</sup>

> 3 Fishing is not longer included as a separate economic activity in national population censuses and surveys. Although some data on the number of fishers is collected and published by the MNRT in their annual statistics,

4
# **2. Data Description**

# **A. LAC data**

In partnership with ASBA, a data request was sent to all the LAC bank supervisors that are ASBA members. The request sought to collect bank-level information on the maturity profile and purpose of loans (that is, commercial, consumer, and mortgage) to the nonfinancial sector. The questionnaire also requested information on bank balance sheet items including information on other loan characteristics such as the currency of denomination and the size or structure of liabilities. Information on profit and loss accounts as well as financial indicators and bank ownership was also requested.<sup>7</sup> The information was requested on an annual basis from 2000 to 2015.

Sixteen LAC countries responded to the survey.<sup>8</sup> While some supervisors provided banklevel information, others provided aggregated information by bank type; for example, Panama provided information across domestic and foreign banks. In terms of the variables requested, overall, the response varied by countries. All countries reported loans by sector, albeit not all countries reported the data across the whole period (for example, Chile only provided annual data starting from 2008). Nine countries provided data on maturity composition, with four countries reporting data on maturity under and beyond one year (maturity pairs), while the rest reported data under one year, between one and five years and over five years (maturity triples). Countries reported remaining loan maturity data, except for Uruguay that reported original loan maturity.

To improve the coverage of countries in our data set, publicly available data on loan maturity and purpose of loans were collected from the websites of central banks and banking supervisory authorities for seven additional countries. In cases where information was not reported in those websites, information was obtained from the notes in the individual bank’s annual financial statements, available on the individual bank’s websites. Maturity data refer to total loans granted by banks, including loans to financial corporations and the public sector. Data on loans by purpose were constructed by aggregating sectoral loan information to compute commercial loans (for example, loans to agriculture, commerce, manufacturing, consumer, mortgage, government,

> 7 See questionnaire in annex 1.

> 8 Cayman Islands responded to the questionnaire but did not provide data by maturity or loan purpose. British Virgin Islands (BVI) provided comprehensive data; since there is no corresponding IFS or World Development Indicators (WDI) data for BVI, the data set does not include BVI.

7
**Table 4 Descriptive Statistics**

|Variable||Mean|Std. Dev|Min|Max|Obs.|Variable||Mean|Std. Dev|Min|Max|Obs.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Log<br>|overall|23.3|2.18|17.6|28.0|N = 339||overall|-0.02|1.01|-2.7|1.9|N = 592|
|(Real<br>Export)|between||2.30|17.7|28.0|n = 131|Regulation|between||1.00|-2.6|1.9|n = 198|
||within||0.08|22.8|23.5|T = 2.6||within||0.09|-0.4|0.4|T = 3|
|log(Real|overall|24.3|2.27|19.2|30.2|N = 531||overall|3.7|0.69|1.7|5.6|N = 476|
|<br>GDP(PPP))|between||2.26|19.2|30.2|n = 180|Finance|between||0.69|1.8|5.6|n = 164|
||within||0.05|24.0|24.6|T = 3||within||0.08|3.2|4.2|T = 3|
||overall|11.2|2.73|3.2|16.7|N = 594||overall|9.4|3.43|2.0|20.0|N = 528|
|log(Area)|between||2.74|3.2|16.7|n = 198|Entry|between||3.37|2.0|20.0|n = 179|
||within||0|11.2|11.2|T = 3||within||0.73|2.7|12.7|T = 3|
||overall|9.0|0.26|8.6|9.7|N = 507|Time to|overall|3.1|0.62|1.6|4.6|N = 509|
|Remote|between||0.26|8.6|9.7|n = 169|<br>Export|between||0.60|1.6|4.6|n = 179|
||within||0|9.0|9.0|T = 3||within||0.13|2.6|4.1|T = 2.8|
|Log(Export|overall|2.0|0.83|-1.7|4.8|N = 338|Infra-|overall|1.3|0.37|0.4|1.9|N = 399|
|<br>Growtht-1,t-2)|between||0.66|0.1|3.8|n = 134|structure|between||0.37|0.4|1.9|n = 133|
||within||0.53|-0.5|4.1|T = 2.5||within||0|1.3|1.3|T = 3|
||overall|1.8|0.66|-3.3|3.4|N =325||overall|6.1|1.82|1.4|9.6|N = 368|
|log(TTRI)|between||0.74|-3.3|3.2|n = 126|Property|between||1.80|1.7|9.4|n = 125|
||within||0.14|0.9|2.5|T = 2.6||within||0.26|5.0|7.0|T = 2.9|
|Log|overall|1.2|0.82|-2.3|3.3|N = 345||||||||
|<br>(MATTRI)|between||0.71|-0.9|2.9|n = 127||||||||
||within||0.44|-0.2|3.4|T = 2.7||||||||

* N: Total number of observations; n: Number of observations per country; T: Number of time periods. The statistics for real export is for 2006-2008 period for all other variables they are for 2005-2007 periods.

# **Table 5 Correlation Matrix**

||Real<br>Export|RealGDP<br>(PPP)|log<br>(MATTRI)|log<br>(TTRI)|Log<br>(Area)|Remote|Exp<br>Grt-1,t-2|Regul.|Finance|Entry|Time to<br>Export|Infrast.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|RealGDP(PPP)|0.95||||||||||||
|log(MATTRI)|**_0.1_**|**_0.03_**|||||||||||
|log(TTRI)|-0.32|-0.24|**_0.02_**||||||||||
|Log(Area)|0.32|0.61|-0.15|0.29|||||||||
|Remoteness|-0.25|-0.31|**_-0.06_**|0.21|-0.09||||||||
|Export|||||||||||||
|Growtht-1,t-2|**_-0.02_**|**_-0.09_**|**_0.04_**|**_0.03_**|**_-0.06_**|**_0_**|||||||
|Regulations|0.58|0.39|0.16|-0.58|-0.26|-0.33|-0.12||||||
|Finance|0.39|0.13|0.11|-0.41|-0.36|**_-0.05_**|**_0_**|0.59|||||
|Entry|-0.27|**_-0.05_**|-0.11|0.35|0.25|0.12|**_0.09_**|-0.49|-0.43||||
|Time to Export|-0.57|-0.4|**_-0.09_**|0.5|0.19|0.27|0.15|-0.78|-0.57|0.42|||
|Infrastructure|0.55|0.43|**_0.02_**|-0.49|-0.28|-0.28|-0.18|0.78|0.61|-0.46|-0.69||
|Property|0.53|0.39|0.12|-0.46|-0.26|-0.24|-0.16|0.87|0.63|-0.58|-0.74|0.85|

* Bold cells show significance levels with p>0.05. The rest is significant at 1 percent.

16
3 may depend crucially on clean water, or gender equality in school enrollment may hinge on access to piped water (facilitating girl child school attendance.) The role of multi-sectoral linkages in the achievement of MDG-like goals has long been recognized (see, for example, Lewis, 1981; De Melo, 1981; and Crosswell, 1981), but it deserves renewed emphasis since they are key to reaching the MDGs, and doing so at potentially lower cost. There are clear gains from multi-sectoral interventions such as maternal and child health interventions (health, nutrition and family planning), as well as from water, sanitation and shelter and other bundled interventions. The effectiveness of multiple interventions in infrastructure, for example, can be shown to yield economies of delivery (Chong and Hentschel, 2003.) Yet inter-sectoral connections are often under-played when designing the average health, education or infrastructure project. For this reason, a renewed emphasis on multi-sectoral factors that affect MDGs is useful.

A key argument presented in this paper is that some of the biggest improvements are likely to come from combining interventions. For example, it is well known that the health impact of improved water and sanitation is much greater when it is accompanied by information on hygiene practices – this is the motivation of the Public-Private Partnership for Handwashing with Soap, a global initiative to promote handwashing with soap to reduce diarrhea.<sup>6</sup> Similarly, Jalan and Ravallion (2001) argue for the need to combine infrastructure interventions with effective public action to promote health knowledge: their research on India shows that diarrhea is much less prevalent and severe among young children in households with piped water, but that this gain largely bypasses children in poor households, particularly when the mother is poorly educated.

Figure 2 provides a simple heuristic illustration of the importance of combining interventions. Using the results from a simple regression, it shows that after correcting for income, countries that had high health care and infrastructure access scores had infant mortality rates on average 25.4 points lower that those who fared badly on both count. Faring well on only health or infrastructure brought about 4.1 and 13.1 point

> 6 A recent review in The Lancet Infectious Diseases Journal _<u>(http://infection.thelancet.com/)</u>_ suggests that a surprising 42-47% of all diarrhea could be prevented if people washed their hands with soap, thereby saving close to one million lives.  There is growing evidence that hand washing also works to prevent acute respiratory tract infections, which are the biggest infectious killers in the world today.  For more information on the Partnership, see: <u>http://www.globalhandwashing.org/</u>
Protection Programs (UDB). We then describe the two main steps in establishing the UDB, a centralized targeting registry of 25 million households ranked according to their socioeconomic status.

# **Social Assistance Programs**

Indonesia’s main social protection programs originate from the social safety nets programs that were launched in 1998 to mitigate the adverse impacts of the Asian Financial Crisis. From the beginning, these programs have adopted decentralized beneficiary selection mechanisms and relied on local leaders and service providers to fine-tune targeting (TNP2K, forthcoming).

The health fee waiver program, known as Jamkesmas, provides access to free services at public health facilities. District governments are responsible for compiling beneficiary lists based on community meetings and on local poverty indicators. This program has a self-targeting feature since households that consider themselves poor, and therefore eligible, can apply to receive a card by producing a poverty statement signed by the village head.

The unconditional cash transfer programs, known as BLT, provided temporary cash compensation to protect poor households against the shocks associated with fuel subsidy reductions implemented in 2005, 2008 and 2013.<sup>8</sup> Two censuses of the poor were used to identify BLT beneficiaries in 2005 and 2008. Households surveyed in these data collection efforts were identified based mainly on subjective consultations between enumerators from the Central Statistical Bureau (known as BPS) and village leaders (see, e.g., SMERU, 2006), and subsequently ranked using a simplified PMT. Registration was a problem. In the absence of pre-existing information, local leaders were left to designate who should be surveyed to be assessed for eligibility for receiving BLT benefits. As a result, the overall geographic allocation did not reflect the geographic distribution of poverty in the country. PMT-based ranking was also a problem, with the use of difficult to verify indicators such as the frequency of buying meat, eating, buying clothes, and the ability to afford medical treatment or the use of credit to meet daily needs. In practice, almost all households registered in these previous

> 8 These cash transfer programs were designed to provide temporary compensation to protect poor households against the shocks associated with fuel subsidy reductions. See Bazzi et al. (2015) for an evaluation of the 2005 program’s impact on household consumption. In 2013, the BLT program was renamed BLSM. For simplicity and since the program is still often referred to by its original name, the acronym “BLT” is used in this paper to refer to all programs.

7
<!-- Start of picture text --> Stock Market Total Value Traded / GDP, by Region<br><!-- End of picture text --> <!-- Start of picture text --> High Income<br>East Asia & Pacific<br>Europe & Central Asia 1983-1988<br>1988-1993<br>Latin America & Caribbean 1993-1998<br>1998-2003<br>Middle East & North Africa 2003-2007<br>South Asia<br>Sub-Saharan Africa<br>0.00 0.20 0.40 0.60<br><!-- End of picture text -->

**Figure 31: Private Credit by Deposit Money Banks - Median Values by Region Over Time (1983-2007)**

<!-- Start of picture text --> Private Credit by Deposit Money Banks / GDP, by Region<br> High Income<br>East Asia & Pacific<br>Europe & Central Asia 1983-1988<br>1988-1993<br>Latin America & Caribbean 1993-1998<br>1998-2003<br>Middle East & North Africa 2003-2007<br>South Asia<br>Sub-Saharan Africa<br>0.00 0.20 0.40 0.60 0.80<br><!-- End of picture text --> 43