rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3: Progress on Malaysia’s National Key Result Areas**

|**2010 Target**|**2012 Target**|**Reported Progress in 2010**<sup>**ix**</sup>|
|---|---|---|
|**REDUCING CRIME**|||
|20% reduction of street crimes|5% annual reduction in overall<br>reported crime|street crime down 37%<br>overall reported crime down 16%|
|**CORRUPTION**|||
|Malaysia’s CPI score at 4.9<br>(from 4.5)||CPI 2010 score 4.4|
|37% approving Government<br>efforts on corruption in TI<br>Global survey (from 28%)<br>Average audit findings per<br>ministry at 10.6 annually<br>(from 11.2)||48% approving government efforts in<br>TI Global survey|
|**IMPROVING STUDENT OU**|**TCOMES**||
|Pre-school enrolments at 72%<br>(from 67%)|Pre-school enrolments at 87%<br>All children having basic literacy and<br>numeracyskills at end ofyear three|72.42% enrolment|
|**POVERTY**|||
|Poverty incidence at 2.8%||(13,471 households removed from|
|(from 3.6%)||poverty)|
|Hardcore poverty at 0% (from<br>0.72%)||Hardcore poverty at 0%|
|**RURAL INFRASTRUCTURE**|||
|Peninsula: 91.4% living within<br>5km of a paved road (from<br>91%)|close to 100% ditto||
|16,000 new/restored houses<br>for rural/hardcore poor<br>_24-hour electricity access:_<br>Peninsula: 99.6% (from 99%)<br>Sabah: 81% (from 77%);<br>Sarawak: 73% (from 67%)<br>_Access to clean/treated water:_<br>Sarawak: 62 % (from 57%);<br>Sabah: 59% (from 57%)|<br>additional 34,000 houses ditto||
||90% ditto<br>90% ditto||
|**URBAN PUBLIC TRANSPOR **|**T**||
|13% using public transport in<br>Klang valley between 0700-<br>0900 (from 10-12%)<br>75% living within 400 meters|25% ditto|17% usage|
|of public transport route (from<br>63%)|||

_Sources_ :

1. TARGETS: Government Transformation Programme Roadmap (January 2010).

2. PROGRESS: <u>http://www.pemandu.gov.my/gtp/, accessed February 8 2011.</u> 9
<!-- Start of picture text --> Policy Research<br>Population, Health, and Nutrillon<br>WPS 868<br><!-- End of picture text -->

- This paper a product of the Population, Health, and Nutrition Division, Population and Human - Resources Department is part of a larger effort in the department to assess disease control priorities in developing countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Otilia Nadora, room S6-065, extension 31091 (March 1992, 35 pages).

In recent years, tetanus and measles are estimated to account for more than 2.5 million childhood deaths annually; measles alone may account for more than 2 million such deaths. Koenig reviews empirical evidence on the most effective and feasible strategies for measles **..id** tetanus vaccination programs.

Koenig found that tetanus and measles immunization programs could significantly reduce deaths among children up to the age of 4 in many developing settings. Vaccinations had a pronounced effect in reducing childhood deaths from measles - with benefits sustained over time, and with the greatest benefits accruing to the most disadvantaged children. He found little support for the existence of a replacement mortality effect.

Studies on maternal immunization against tetanus showed a great reduction in the number of neonatal deaths, but considerable uncertainty about the number of doses needed and how long the immunity lasted. Recent evidence suggests that giving the mother two doses of tetanus toxoid may confer significant levels of protection against neonatal death from tetanus for 15 years or more. Evidence on the impact of a single dose is less conclusive.

The Policy Research Working Paper Series disseminates the findings of work under way in theBank. An objective of thc series is to gct these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
the rate of inflation, coupled with the weaker dollar. It would appear, however, that with the substantial increase in the price of natural gas to producers in Russia, prices are much closer to LRMC in 2007 than when we conducted this analysis based on 2001 data. Moreover, with its decree #333 in May 2007, the Government of Russia announced plans to increase the price of natural gas to industrial users to international levels by 2011, less transportation costs and export taxes. In early 2008, prices on exports to Europe were about $378 per TCM. With transportation costs of about $35 per TCM and export taxes at 30 percent, to implement this plan today, prices in Russia would have to rise to about $225 per TCM. Russian government forecasts of domestic natural gas prices in 2011, however, are that prices would rise to about $120 per TCM. Thus, to implement this plan, Russian domestic market prices would have to rise dramatically more than what is planned. More importantly, such high domestic prices would be very inefficient. High prices would induce very significant reductions in Russian demand, to the point where the value to Russian consumers would be considerably greater than the long run marginal costs of production. This would imply substantial monopoly profits for Gazprom on domestic sales. Russia fought a bitter battle at the WTO and won the right to have dual pricing of natural gas. However, except for the 30 percent export tax difference and the transportation fees, Russian current plans call for it to unify natural gas prices for its industrial users.

9
Table 6. Heckman probit (Heckit) and Heckman selection models assuming 40 degree Celsius definition of cooking events

||Heckit|H|eckman|
|---|---|---|---|
||(Regular use of<br>|(Actual frequenc|y of stove use per week)|
||≥ 2x/week)|||
|Incentive Period 1|-0.209<br>|-0.943<br>||
||(0.205)|(0.320)***||
|Incentive Period 2|-0.049|-0.713||
||(0.207)|(0.389)*||
|Incentive Period 3|-0.023|-0.011||
||(0.199)|(0.421)||
|Incentive Period 4|0.003|-0.449||
||(0.200)|(0.395)||
|Incentive Period 5|-0.115|-0.783||
||(0.232)|(0.360)**||
|Pay Treatment Period 1|-0.688|-1.376||
||(0.2090<sup>***</sup>|(0.329)***||
|Pay Treatment Period 2|-0.484|-1.148||
||(0.202)<sup>**</sup>|(0.345)***||
|Pay Treatment Period 3|-0.236|-0.379||
||(0.197)|(0.346)||
|Pay Treatment Period 4|-0.199|-0.586||
||(0.199)|(0.339)*||
|Pay Treatment Period 5|-0.046|-0.672||
||(0.219)|(0.340)**||
|Free Treatment Period 1|-0.377|-0.863||
||(0.200)<sup>*</sup>|(0.335)***||
|Free Treatment Period 2|-0.110|-0.551||
||(0.201)|(0.338)||
|Free Treatment Period 3|0.193|0.150||
||(0.202)|(0.389)||
|Free Treatment Period 5|-0.074|-0.337||
||(0.239)|(0.394)||
|Group training dummy|Yes||Yes|
|Socio-economic variables|Yes||Yes|
|Cooking characteristics|Yes||Yes|
|Constant|0.002|2.013||
||(0.239)|(0.481)***||
|N|1498||1,498|
|Wald test of independent<br>equations|Prob > Χ<sup>2</sup>= 0.42|Prob|> Χ<sup>2</sup>= 0.49|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01.  Robust standard errors in parentheses. Free treatment period 4 is the reference group. Selection equation presented in Table 2.

23
# **References**

- Abadie, A., Chingos, M. M., and West, M. R. (2018). Endogenous stratification in randomized experiments. _Review of Economics and Statistics_ , 100(4):567–580.

- Ahmed, S., McIntosh, C., and Sarris, A. (2020). The impact of commercial rainfall index insurance: Experimental evidence from Ethiopia. _American Journal of Agricultural Economics_ , 102(4):1154–1176.

- Ali, W., Abdulai, A., and Mishra, A. K. (2020). Recent advances in the analyses of demand for agricultural insurance in developing and emerging countries. _Annual Review of Resource Economics_ , 12:411–430.

- Andrews, I. and Kasy, M. (2019). Identification of and correction for publication bias. _American Economic Review_ , 109(8):2766–94.

- Andrews, I. and Oster, E. (2019). A simple approximation for evaluating external validity bias. _Economics Letters_ , 178:58–62.

- Athey, S., Chetty, R., and Imbens, G. (2020). Combining experimental and observational data to estimate treatment effects on long term outcomes. Mimeo.

- Bandiera, O., Fischer, G., Prat, A., and Ytsma, E. (2021). Do women respond less to performance pay? Building evidence from multiple experiments. _American Economic Review: Insights_ , 3(4):435–54.

- Bédécarrats, F., Guérin, I., and Roubaud, F. (2020). _Randomized control trials in the field of development: A critical perspective_ . Oxford University Press.

- Belissa, T., Bulte, E., Cecchi, F., Gangopadhyay, S., and Lensink, R. (2019). Liquidity constraints, informal institutions, and the adoption of weather insurance: A randomized controlled trial in Ethiopia. _Journal of Development Economics_ , 140:269–278.

- Belissa, T. and Marr, A. (2018). Effects of bundled index-based insurance with credit and agricultural inputs on uptake, investment, productivity and welfare of smallholder farmers in Ethiopia. Mimeo.

- Berhane, G., Dercon, S., Hill, R. V., and Taffesse, A. (2015). Formal and informal insurance: Experimental evidence from Ethiopia. Mimeo.

- Bertram-Huemmer, V. and Kraehnert, K. (2018). Does index insurance help households recover from disaster? Evidence from IBLI Mongolia. _American Journal of Agricultural Economics_ , 100(1):145–171.

- Binswanger-Mkhize, H. P. (2012). Is there too much hype about index-based agricultural insurance? _Journal of Development Studies_ , 48(2):187–200.

- Bo, H. and Galiani, S. (2021). Assessing external validity. _Research in Economics_ , 75(3):274–285.

23
# Table 1 – Construction of Variables

||**Risk Type**<br>**I, II, III**|**If variabl**<br>**in the**|**e appears**<br>**data set**|**Used in**<br>**the**|
|---|---|---|---|---|
|||**Chile**|**Mexico**|**cluster**<br>|
|||||**analysis**|
|**Behaviors/outcomes**|||||
|Not idle – either in school nor working|II, III|X|X|X|
|No early school dropout (i.e. completed secondary school)|II|X|X|X|
|Literate (can read and/or write a message)|III||X|X|
|In school|II, III|X|X||
|Years of education completed|I, III|X|X||
|<br>Working (Not Working<sup>c</sup>)|<br>II, III|X|X||
|Older age when started working|II, III|X|X|X|
|Safe sex (among the sexually active)|II|X|X||
|Low number of sexual partners in the past year|II||X<sup>a</sup>||
|<br>Older age at onset of sexual activity|II|X|X<sup>a</sup>|X|
|<br>Older age at first pregnancy/child|III|X|X<sup>a</sup>|X|
|<br>Has at least one child (Does not have at least one child<sup>c</sup>)|III|X|X||
|<br>Married (Not Married<sup>c</sup>)|I, III|X|X|X|
|<br>Participate in extracurricular activities|II|X|X|X|
|<br>Registered to vote, planning on voting in the next election,|III|X|X<sup>b</sup>|X|
|desires to vote when of age (if under 18 years old)<br>Has not been a victim of a crime (proxy for criminal activity)|II|X||X|
|Attitude towards drugs (respondent can justify using drugs)|II||X<sup>a</sup>||
|<br>Attitude towards alcohol(respondent canjustify gettingdrunk)|II||X<sup>a</sup>||
|**Protective Factors**|||||
|Trust in governmental institutions|I|X<sup>d</sup>|X<sup>a,e</sup>|X|
|<br>Trust in community institutions|I|X<sup>f</sup>|X<sup>a,g</sup>|X|
|<br>Connected (whether youth reaches out – for talk or help- to|I|X|X|X|
|someone when they have problems)|||||
|<br>Living with both, one or no parents|I||X|X|
|<br>Positive relationship with father|I|X<sup>h</sup>|X<sup>i</sup>|X|
|Positive relationship with mother|I|X<sup>j</sup>|X<sup>k</sup>|X|
|<br>Connected with an adult other than parents|I|X||X|
|Communication with parents (talk to parents when facing a|I||X||
|personal problem)|||||
|<br>Church attendance|I|X|X|X|
|Spiritual influence in beliefs, opinions and attitudes|I||X||
|<br>School quality<sup>l</sup>|I||X|X|
|<br>Feeling optimistic about future work|I|X|||
|Feeling prepared for future employment|I|X|||
|<br>Sense of wellbeing (level of happiness reported bytheyouth)|I||X<sup>a</sup>||
|**Risk Factors**|||||
|Have felt discriminated against|I|X|||
|<br>Parental education – this is a good proxy for poverty|I|X|X|X|
|Household ownership of durable/luxury goods<sup>m</sup>|I||X||
|Monthly earnings of heads of household|I||X||
|<br>Limited access to healthcare|I||X|X|

24
movement of goods and which is recognized on its behalf or on behalf of a national customs administration for complying with supply chain security standards. To qualify as an AEO, it is necessary that the company meet the criteria specified by the Customs of the country in question, including having appropriate records of compliance with customs requirements and a satisfactory management system of commercial records (SÁ PORTO et al., 2013, p. 64).

Another important soft measure is the Single Window (SW). It is a single point of contact between foreign trade operators and government to fulfill the requirements for import, export and customs transit. Through the single window, all demands for information and documents made by the bodies involved in the trade are given in a coordinated and harmonized manner through a single interlocutor, preferably in a computerized system accessible over the Internet. Responses from government agencies to foreign trade operators must also occur in a coordinated manner through this system (SCORZA; MACEDO, 2013, p.38). Additionally, another important TF measure is the Mutual Recognition Arrangement (MRA), which is an international arrangement based on an agreement by which two or more countries agree to recognize one another's conformity assessments regarding TF measures, such as the AEO.

The objective of this paper is to analyze the impacts of those selected trade facilitation measures on international trade flows. For that we used a gravity model, such as in Wilson, Mann and Otsuki (2005) and in Souza and Burnquist (2011). However, we used a more recent data set, a panel that included trade data from 2011 and 2012 for 72 countries. Moreover, to measure the impacts of trade facilitation measures, we included dummy variables for those three trade facilitation measures mentioned above, the Authorized Economic Operator (AEO) program, the Single Window (SW) program and the Mutual Recognition Arrangements (MRA). Those three variables were used as a proxy for trade facilitation.

The paper is structured as follows. After this brief introduction, we review in Section 2 the recent literature on trade facilitation and on the gravity model, while in Section 3 we present the model and the data used in this paper. We present our main results In section 4, and in section 5 we present our conclusions and possible further research on this subject.

3
on assortative matching.

The following section describes the setting and our empirical strategy in more detail.

# **3 Empirical Setting**

Our partner institution, Saath, is a non-government organization based in Ahmedabad, India. Founded in 1989, Saath implements development initiatives in slum communities, including health, education, infrastructure improvement, job placement, and livelihood training programs. Additionally, Saath provides credit and savings services to the urban poor through its Microfinance Unit. In 2009, Saath Microfinance had over 6,400 active clients in 4 branches, with a savings portfolio of INR 18 million (USD 390,000) and a loan portfolio of INR 19 million (USD 410,000).<sup>3</sup> While Saath has provided mentoring support to community-based credit and savings groups since the mid-1990s, its Microfinance Unit was not formally established until 2002. In that year, Saath integrated the credit and savings groups it previously mentored into its organization, as well as registered them as cooperative societies with the Indian government. Saath also began managing these credit and savings cooperatives at this time, giving way to the Saath Microfinance Unit. Today, Saath Microfinance provides various financial services to slum communities, including voluntary savings accounts, compulsory savings accounts, and group liability loans.

## **3.1 Savings Products**

Since its inception in 2002, Saath Microfinance has been offering voluntary savings accounts to its clients. These voluntary savings earn an interest of 6% per year and do not have a minimum balance. As the name suggests, members are not obliged to make regular deposits in voluntary savings accounts. Any amount may be deposited, but only six withdrawals per year may be made.

In November 2007, Saath Microfinance initiated compulsory savings accounts among its members. Specifically, members are required to deposit INR 100 (USD 2) every month into compulsory savings accounts, for the duration of their membership with Saath Microfinance. Clients may withdraw any amount from their compulsory savings at any time, as long as a minimum balance of INR 3,500 (USD 70) is maintained. Similar to voluntary savings, compulsory savings earn an interest

> 3Based on Saath’s 2008-2009 Annual Report.

7
strongly regressive across districts. The strong cross-district regressivity in applications is effectively neutralized by the formula that allocates funding across districts, which fixes the amount of money available per district at the outset of the entire program. The progressivity injected into project selection is entirely cross-district, suggesting that it is in fact the _centralized_ component of this decentralized program that makes it mildly pro-poor. It is also noteworthy that variables, such as access to information and political activity are not correlated with the funding decisions conditional on applications, consistent with the fact that the centralized funding formula to the districts did not include these characteristics.

Figure 1 illustrates how the regressive nature of the decentralized applications process is counteracted by the progressive centralized district funding formula. Here, we plot the smoothed number of applications and the acceptance rate over the distribution of poverty headcounts across wards. The heterogeneity is tremendous: the richest wards submit almost six applications per 1,000 people compared with less than two among wards with the highest poverty rates. The acceptance rates, on the other hand, increase from below 12% to over 15% across that same span. This finding suggests that the heterogeneity in applications is not simply arising from the expected probability of funding: application rates are highest in places where the funding rates are lowest and vice versa.

# **6. Targeting Performance at the Village Level**

Our analysis of within-village targeting focuses on the Vulnerable Groups component (VG projects) of TASAF II because the data collected for this study was part of baseline data collection for the randomized study designed to assess the impact of this component on the creation of small enterprises and household welfare.<sup>13</sup> The data at the household level allow the cross-examination of the earlier findings from the institutional data that we have used up to this point, because the surveys were conducted by an independent firm. To the extent that data coming from the program itself may contain accounting irregularities (ghost projects, efforts to cover up the misuse of funds, etc.), our own data collection allows us to independently verify the nature of targeting performance under this component of TASAF II.

> 13 Vulnerable Groups projects provide grants of 6-10 thousand dollars to groups of roughly 10 individuals from vulnerable households in order to finance an entrepreneurial activity. Typical activities for these groups are animal husbandry, beekeeping, tailoring, or the operation of milling machines. These projects thus generate an unusually private form of benefit relative to typical CDD programs.

15
_Country by country results_ It is possible that there are too many confounding factors that would be obscuring a significant relationship. In particular, country-to-country differences in the exposure to malaria, in the support of the health care system, or relative position in the wealth distribution might not be well captured in the pooled model above (despite the dummy variables for subregions). In addition, the pooled approach to estimating wealth might overemphasize differences between countries compared to smaller differences within countries. Therefore, these country-by-country regressions used a wealth index that was recalculated country by country. Table 9 reports the selected p-values of the joint tests of significance of the wealth and cluster-level wealth and fever variables in a model that also includes all the other control variables (see Table 8) and that allow all the coefficients to differ across countries.

**Table** 9. **P-values of tests on wealth, average cluster wealth, and average cluster fever variables in country-by-country probit regressions of fever**

|_Country_|_Joint_<br>_teston_<br>_wealth_<br>_and_<br>_wealth_<br>_squared_|_Jointtest_<br>_oncluster_<br>_wealth_<br>_and_<br>_cluster_<br>_wealth_<br>_squared_|_Average_<br>_cluster_<br>_fever_|_Country_|_Jointtest_<br>_on_<br>_wealth_<br>_and_<br>_wealth_<br>_squared_|_Jointtest_<br>_oncluster_<br>_wealth_<br>_and_<br>_cluster_<br>_wealth_<br>_squared_|_Average_<br>_cluster_<br>_fever_|
|---|---|---|---|---|---|---|---|
|Benin1993|0.403|0.421|0.072**|Comoros1996|0.567|0.848|0.097*|
|BurkinaFaso1999|0.701|0.677|0.000**|Kenya1998|0.730|0.044**|0.001**|
|C.A.R.1994-95|0.313|0.525|0.025*|<br>Madagascar1997|0.824|0.438|0.001**|
|Chad1996|0.082*|0.307|0.000**|Mozambique1997|0.978|0.450|0.900|
|C6ted'lvoire1994|0.195|0.133|0.245|<br>Malawi1996|0.948|0.996|0.067*|
|Cameroon1998|0.069*|0.388|0.000**|Rwanda1992|0.559|0.685|0.001**|
|Ghana1998|0.431|0.137|0.172|Tanzania1996|0.475|0.022**|0.054*|
|Mali1995-96|0.773|0.160|0.000**|Uganda1995|0.253|0.902|0.000**|
|Niger1997|0.604|0.685|0.000**|<br>Zambia1996-97|0.175|0.276|0.000**|
|Nigeria1999|0.529|0.055|0.000**|Zimbabwe1999|0.869|0.583|0.125|
|<br>Senegal1992-93|0.423|0.166|0.000**|||||
|Togo1998|0.910|0.848|0.000**|||||

_Note: *(**)_ indicates underlying variables that are significantly different from zero at the 10(5) percent level. _Source:_ Authors' calculations from DHS data. Model includes individual, household and cluster variables as well as dummy variables for sub-regions (not reported, see Table 8 for list of variables). P-values are calculated adjusting for clustering.

Again, the results do not provide evidence for a strong relationship between reported fever and poverty. Wealth-both at the household and cluster levels-are typically not statistically significantly associated with fever. Chad and Cameroon are exceptions where household wealth is significant, and Kenya and Tanzania are

> 19. An alternative specification that includes wealth as dunmmy variables for quintile produces results that are qualitatively similar.

_18_