rafmacalaba/datause-encoder-data huggingface dataset:
# Table I.4. Robustness Analysis for the Employed Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.173|0.235|0.174|0.154|0.191|0.150|0.173|0.235|0.174|0.154|0.191|0.150|
||(0.147)|(0.173)|(0.150)|(0.146)|(0.159)|(0.148)|(0.147)|(0.173)|(0.150)|(0.146)|(0.159)|(0.148)|
|Obs. left|470|363|458|454|365|449|470|363|458|454|365|449|
|Obs. right|955|655|901|1,214|1,080|1,144|955|655|901|1,214|1,080|1,144|
|Mean values (Non-RAMV)|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.261|0.081|0.243|0.192|0.083|0.158|0.293|0.294|0.339|0.297|0.214|0.305|
||(0.248)|(0.212)|(0.237)|(0.187)|(0.177)|(0.186)|(0.285)|(0.285)|(0.296)|(0.206)|(0.208)|(0.206)|
|Obs. left|401|425|411|379|425|411|401|425|411|379|425|411|
|Obs. right|710|756|731|1,788|1,560|1,663|710|756|731|1,788|1,560|1,663|
|Mean values (Non-RAMV)|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|
|_Panel C: Polynomial of order two_|||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.188|0.433|0.244|0.300|0.424|0.393|0.230|0.085|0.339|0.297|0.298|0.350|
||(0.261)|(0.371)|(0.293)|(0.240)|(0.262)|(0.250)|(0.319)|(0.330)|(0.296)|(0.292)|(0.260)|(0.303)|
|Obs. left|557|365|474|522|365|452|557|365|411|522|365|452|
|Obs. right|1,080|661|968|1,560|1,739|1,563|1,080|661|731|1,560|1,739|1,563|
|Mean values (Non-RAMV)|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|0.561|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
20 to evaluate will be objectified and "wired" into expert systems and intelligent workstations at individual banks. As we have seen, expert systems now exist that estimate the risk embodied in individual loans. The process of aggrega1.&g these estimates into measures of a bank's overall pofQjol risk is an exercise in applied portfolio theory that the Basle Agreement has yet to address. The resolution of this aggregation problem, when it is addressed, will turn on being able to esmate the extent of pairwise correlations between returns on individual loans.

Whether or not concentration risk is measured formally, it is reasonable for government examiners to develop their own standardized expert system as a benchmark for analyzing the loan portfolios of individual banks. It is also reasonable for them to seek to co-ordinate and standardize system architecture and inforTnation file structure across banks. This is true whether or not the bank being examined yet uses computer software to conduct credit analysis. In developed and developing countries alike, examiners and top bank managements share an interest in automating bank credit files and internal risk analyses at reasonable speed. At a minimum, loan deWisions need to be linked specifically to an information file of borrower characterisdcs and prospects. This file begins with the data would-be borrowers furnish in loan applications, but this database needs to be updated routinely as time passes and loan proceeds are collected or delayed. As a country's banks convert to electronic files, monitoring costs can be minimized if supervisors insist that banks cooperate in designing ways to input their files into "transportable" private credit-rating and govemment examination systems.

Banks and supervisors in G- 10 counties must make progress on these problems before authorities in developing countries can be expected to do much about them. But the systems developing countries adopt today should be made ready to incorporate systemic changes that can be confidently predicted to occur in institutions and countries that are technologically more advanced.

G- 10 countries need to set up (and therefore may **be** expected to establish) three separate but complementary information subsystems. These subsystems would respectively track: (a) the market values of securities that trade in secondary markets; (b) changes in the repayment prospects of loans; and eventually (c) the effects of changes in interest rates and foreign-exchange rates on the value of the institution and its economic income. As they build shese subsystems, economic efficiency dictates that the valuation
20 2. Data Collection **and** Sources 22.

22. The initial work consisted of collating relevant information available from Bank files in Washington, reviewing this and extracting from it the required road revenues and expenditures, profit and loss, balance sheet and funds flow data, and pertinent points on their compilation. In the main, the infonmation sources were secondary (Bank reports which included summaries of road revenues and expenditures, and trading and balance sheet analyses) rather than primary sources (government accounts, annual reports and audited accounts).

23. This initial tranche of work yielded about half the data required. Obtaining the remainder, so that the gaps could be filled, proved to be a slow process that took about a year. Some of the data were obtained direct from the head offices of transport enterprises, although most was collected by bank personnel on mission and staff from the Bank's field offices. In one case, a consultant was used to collect the necessary financial data in the field.

24. Naturally enough, the sources of data regarded as the most reliable were annual accounts, complete with notes on accounting policies and the compilation of specific items, audited by an international firm of public accountants. Unfortunately, only a small minority of the figures presented in this report have been extracted from such ideal sources. Other sources used consisted of:

- (i) Audited annual accounts without explanatory notes.

- (ii) Annual accounts audited by the local auditor-general's departments.

- (iii) Draft and unaudited accounts.

- (iv) Incomplete accounts, usually consisting of a profit and loss account only.

- (v) Financial planning and budgeting documents.

- (vi) Complete or summarized annual accounts quoted in Bank or consultants' reports.
# **5 Outcomes**

The unit of treatment assignment is the community, and so some care needs to be taken in constructing outcome measures. The reason is that while the communities that we have sampled are fixed relative to their proximity to deployments, individuals and households are not. People relocate, being pushed by insecurity and pulled by opportunity. To focus on outcomes that adhere to individuals or households rather than communities may be fallacious. For example, suppose we wanted to study whether peacekeeping base proximity affects the likelihood of violent victimization. Suppose then that we simply ask individuals whether they had been victimized in recent years, and then compare the responses of those residing in deployment base versus non-base communities at the time of fieldwork. We may find that respondents in deployment base communities report victimization in recent _years_ at a higher rate than in non-base communities. But this may be because they relocated to the base communities from elsewhere due to the security of the base community relative to their home community. Higher rates of victimization responses in deployment communities may reflect the fact that these places are _more_ secure, not less. If one failed to take such mobility into account, one might draw the wrong conclusion.

Keeping these perils in mind, we use a set of indicators that attempt to capture community-level security conditions as reflected in people’s behavior. As such, these indicators provide ostensibly objective reflections of risks and behavioral responses to perceived risks. These indicators are also typically of intrinsic interest in policy evaluations (Mvukiyehe and Samii 2010). With respect to security, we have (with data sources shown in parentheses),

- Victimization by looting or physical attack, or fear of such victimization, occurring in the current community in which one is residing at the time of interview (household survey).<sup>17</sup> Security from PKO deployments should result in lower rates of victimization occurring in

> 17We have demonstrated in previous work that incidences of victimization by armed groups in the post-deployment period were extremely rare (Mvukiyehe and Samii, 2010). Therefore, we only focus on isolated criminal violence.

xiv
# **5.  Summary and Conclusions**

In this paper, we have used high-resolution data from Hansen et al (2013) to take a new look at the effectiveness of protected areas in slowing tropical forest clearing. Our measure of effective protection for each park is the ratio of deforestation rates in boundary zones that extend 10 km outside and inside the park’s boundary. We compute deforestation rates from data at 30 m resolution for 4,028 parks in 64 countries in Asia/Pacific, Africa and Latin America. For each park, we construct an annual time series of outside/inside rates for the period 2001-2012.

For estimation work, we join our Hansen panel to extensive data on park characteristics from the WDPA database. We use two sets of panel estimates to test associations between park characteristics and our measure of protection. In the first exercise, we use random effects to test the effect of parks’ age, size, IUCN status, management mode and legal status on forest protection. We find very high significance for some of these variables, but no significance for many of them. Particularly important variables in this context are park size, national park status, and management by indigenous peoples. They all have large, positive, highly-significant associations with effective protection across regions. In a supplementary test, we also find a significant, positive impact for “species charisma” in Asia/Pacific, embodied in higher effective protection for parks that include tiger habitat.

In the second exercise, we use fixed-effects estimation to focus on the effect of park establishment on protection. Our estimation work is confined to the period after 2000, since that is the temporal span of the Hansen data. We extract protected areas established since 2002 from the database, yielding a sample of 726 parks. Then we test establishment effectiveness with a variable for each park whose value is 0 for the period from 2001 until the establishment year and 1 thereafter. Our 19
However, the study by Van Doren, Hoag and Field (1999) finds that committee membership does confer benefits to Senators. They compute PAC spending by sub-sectors of agriculture during 1989–94 with the objective of studying what characteristics of Senators influence the pattern of contributions. They find membership on the Senate Agriculture Committee and the Agriculture, Rural Development and Related Agencies sub-committee of the Senate Appropriations Committee to be the most important determinant. Membership on the Senate Agriculture committee confers the ability to receive $13,000 more in agricultural PAC contributions, and membership on the Appropriations Sub-committee confers an advantage of over $4,000. Some agricultural sub-sectors contribute far more to committee members than do others. The study also finds that while the average legislator only received about 7% of his or her PAC funding from Ag PACs, agricultural committee members receive almost 14% of their PAC funding from Ag PACs. Of all the sub sectors, food manufacturing valued membership to agricultural PACs the most, paying on average $2,332 per year to committee members. Next in line were sugar cane and sugar beet producer PACs, at $1,617, followed by agricultural services at $1,288. Membership to Agriculture Appropriations sub committee also mattered, leading to an aggregate annual contribution of $4,215 more than non-members. Agricultural services PACs were the leaders in this category, paying on average $1,094 more, followed by dairy PACs at $ 891. Van Doren et al. also find the percentage of state under farmland to be a consistent indicator of PAC contributions. Thus with every 1% increase in % farmland, holding everything else constant, the senator from that state could expect to get $124 more per year. Sugar cane and sugar beet ($27) and tobacco ($22) were the highest payers. Dairy producers came in third at $18.

While their estimates are statistically significant, the effects are small relative to the total campaign contribution these committee members receive. The lobbying data we have analyzed suggests that a similar exercise for House Agriculture committee members would show committee-effects that are not only statistically significant, but also economically large.

To close, it is pertinent to ask whether informational lobbying might not be a useful alternative to the quid-pro-quo lobbying of the Grossman-Helpman model. It has been well documented, for example in Parker and Parker (1998) that congresspersons and lobbies interact continually on agricultural issues. Congresspersons have full knowledge about their constituents’ preference, which is why they seek out memberships on agriculture committees.

44
_n_ 13 _c H C_ the price of the nutritional input ( _pt_ ) , and child ( _X )_ , household ( _X_ ), and community ( _X_ ) characteristics that also vary over time<sup>14</sup> . Child’s health status ( _Htc_ ) is a dummy indicating that the child suffered a life-threatening illness<sup>15</sup> . This is an endogenous variable because it is a choice and is likely to be affected by public spending. Hence, I instead use in the regression its predicted value ( _H_<sup>ˆ</sup> _ijt c_ ), where the predictors of an episode of a serious illness are the intensity of air and water pollution in the community. My second stage equation is where _N_<sup>_c_</sup> _ijt_<sup>is the height-per-age z-score level for the child andθ</sup> _i_<sup>represents the individual fixed</sup> effects. μ _ijt_ represents unobserved attributes related to both the mother and the child, such as resilience, maternal initiative, and determination. I use a first difference approach and cluster the standard errors at the region-year level to account for any variation within region and year<sup>16</sup> .

# _Main Variables_

The nutritional status of the child is measured using height z-scores. Height has proven to be an informative long-term indicator of the nutritional status of children. Z-scores are constructed using the new World Health Organization (WHO) international child growth

> 13 The price chosen is the price of rice, the main staple in Peruvian diets. Some of the rice prices were missing, so they were imputed using the province averages to replace the missing values. 14 I did not include maternal characteristics that vary over time because the only one I could control for, maternal education, had coding issues between rounds.

15 Using acute health conditions instead of minor episodes allowed me to avoid capturing seasonal health problems and to reduce potential biases in reporting health conditions.

16 The clustering level is the region-year. Hence, clusters are defined for non-movers as well as for movers. Thus, there is a cluster for children who lived in Lima in 2002 and stayed in Lima in 2006, and there are different clusters for children who lived in Lima in 2002 but moved to other regions in 2006.

11
# **Table 2b: The Impact of Local Elections (PILKADA) on District Government Revenue and Fiscal Balance per capita**

# **by Region**

Treatment: T =1 if district held direct election in 2005; T = 0 if district did not have direct election during 2005-07

|_Variable_<br>_Coeffi-_<br>_cient_|_Revenue from own sources_<br>_Budget surplus_|
|---|---|
|**Districts outsideJa**|**va and Bali vs. Comparison Districts**|
|T*D06<br>|45,725***<br>1.056e+06***|
||(14,764)<br>(348,511)|
|# obs|605<br>607|
|# districts|130<br>130|
|**Districts inJava a**|**nd Bali vs. Comparison Districts**|
|T*D06<br>|-6,532<br>312.5|
||(5,496)<br>(23,262)|
|# obs|567<br>569|
|# districts|118<br>118|
|Notes:||

*: 10% level of significance; **:  5% level of significance; ***: 1% level of significance Standard errors in parentheses estimated based on fixed-effects model with an AR(1) disturbance (Baltagi and Wu, 1999)

Districts that held direct elections during 2006 & 2007 are dropped from the sample used in the regressions 34
returns – if they are allowed to come to fruition at all. Moreover, progressively weaker human capital in both public and private sectors may result in lower returns (fewer good ideas) from a given amount of R&D investment (Goni, Lederman and Maloney, 2011). The section begins by examining the need for strengthening skills to address this key constraint to the accumulation of knowledge, and then discusses a few policies to address the more general business environment constraints impeding experimentation, global learning, and talent attraction and retention.

# **_3.3.1 Strengthen education and skills for green innovation_**

# **Figure 11.** **<u>Number of Researchers</u>**

<!-- Start of picture text --> A. Researchers by R&D performing sector, 2009 B. % Researchers who are Female<br>(Per thousand total employment in the economy)<br>ISL<br>FIN PRT<br>DNK<br>EST<br>NZL (2007)<br>SWE SVK<br>JPN RUS<br>NOR ISL<br>KOR (2008)<br>ZAF (2008)<br>USA (2007)<br>PRT POL<br>FRA (2008) GBR<br>CAN (2008) ESP<br>AUT<br>GRC (2007)<br>AUS (2008)<br>BEL TUR<br>GBR Business SVN<br>DEU enterprises NOR<br>IRL Higher education<br>SWE<br>SVN<br>EST Government ITA<br>ESP IRL<br>LUX Private non-profit HUN<br>RUS<br>FIN<br>SVK<br>CHE (2008) Unclassified BEL<br>CZE DNK<br>NLD CHE (2008)<br>HUN<br>CZE<br>GRC (2007)<br>ITA CHL (2008)<br>POL FRA (2008)<br>TUR<br>AUT<br>CHN<br>ZAF (2008) NLD<br>CHL (2008) LUX<br>MEX (2007) DEU<br>0 5 10 15 20 KOR (2008)<br>JPN<br>0% 10% 20% 30% 40% 50%<br>Source: OECD, Main Science and Technology Indicators Database, June 2011.<br><!-- End of picture text --> 33
25 hardly be affected by the tax<sup>include the Chemical (1.2%), Mechanical (2.4%)</sup> and Transport Material (1%)<sup>industries as well as retail trade and the</sup> financial sector. While the last two<sup>sectors are not really capital intensive</sup> and hence should not be expected<sup>to be dramatically affected by an asset based</sup> tax, the result is more surprising<sup>for some of these industries where capital</sup> is a crucial factor of production.

To put all this in perspective,<sup>average corporate tax rates in Brazil</sup> range between 10 and 18% (between<sup>1981 and 1988), as opposed to the 35% flat</sup> rate assumed in our analysis.<sup>None of the factors affecting our estimate of</sup> the average corporate tax rate<sup>would affect the MAT (in fact that is the point</sup> of a MAT); thus the revenue<sup>gain from a MAT could easily be four to five times</sup> higher than suggested in Table<sup>6.</sup> <!-- Start of picture text --> iR vrmcus AMwor Rotsof Renl<br>3. /<br>_L_ /<br><.6 o04 0.4 o oDA t.2<br>Log Stwrd DeaIotTo<br>-____________ __<br><!-- End of picture text --> Figure 1 A common argument to justify the introduction of a minimum tax is that it can reduce intersectoral distortions. The Brazilian simulation show that when assets are used as<sup>a</sup> base for a minimum tax, this does<sup>not</sup> necessarily hold. The standard deviation of marginal effective<sup>tax</sup> rates increases from .31 to .35<sup>with</sup> the introduction of a 2% minimum<sup>tax.</sup> This reflects the wide variety of capital intensity in modes of production accross sectors. The<sup>larger the capital intensity, the higher thc</sup> effect of an asset based tax.<sup>This may be explained by the distortion the tax</sup> creates against capital intensive<sup>sectors as illustrated by the following</sup> example. Minerals extraction,<sup>the wood processing industry and the rubber</sup> industry face the same degree<sup>of uncertainty, with a standard deviation of</sup> 0.37. The revenue gain from the<sup>minimum tax is the largest for the sector with</sup> the lowest rate of return, minerals<sup>and the lowest for the sector with the</sup> highest rate of return, rubber.<sup>Mineral also happens to be the more capital</sup> intensive of the three sectors.

The final result is perhaps the<sup>most interesting one. We already pointed</sup> out that a higher variance raises<sup>th? impact of a MAT, but that a lower</sup>
# **The Financial Risk Reduction Provided by Ghana’s National Health Insurance Scheme**

Dhushyanth Raju Stephen D. Younger _Key words_ : out-of-pocket expenditures, public healthcare, health insurance, financial risk protection, Ghana, Africa _JEL codes_ : I13; I15; H51 Raju, World Bank, <u>draju2@worldbank.org; Younger, Commitment to Equity Institute, sdy1@cornell.edu. The study</u> was prepared under the World Bank’s advisory services and analytics task _Ghana Human Development Public Expenditure Review_ (P175117). We thank Jeremy Barofsky, Jishnu Das, Jeffrey Hammer, Magnus Lindelow, Patrick Mullen, and Paul Andres Corral Rodas for comments. We also thank Enoch Oti Agyekum and Alex Nartey for comments and assistance in obtaining relevant data and documentation.