rafmacalaba/datause-encoder-data huggingface dataset:
15

**Table 2:  Competitive Elections and Public Policy in Poor Countries**

<u>(</u> _<u>p</u>_ -values in parentheses, <u>standard errors clustered by country)</u>

|Dependent<br>Variables:|Gross<br>Secondary<br>School<br>Enrollment|Gross<br>Primary<br>School<br>Enrollment|Private<br>Investment/GDP|Bureaucratic<br>Quality<br>(ICRG)|<br>Corruption<br>(ICRG)|
|---|---|---|---|---|---|
|Competitive|2.51|4.82|0.07|0.01|0.05|
|elections, 0-1|(0.34)|(0.19)|(0.94)|(0.95)|(0.76)|
|Income/capita,|48.58|61.00|10.64|2.77|0.55|
|real, ppp-<br>adjusted, in<br>$10,000|(0.00)|(0.00)|(0.03)|(0.01)|(0.40)|
|Fraction|-14.36|16.74|2.30|1.96|0.17|
|population<br>rural|(0.40)|(0.40)|(0.58)|(0.03)|(0.81)|
|Fraction|-238.92|-81.96|-35.81|-1.58|1.04|
|population<br>young|(0.00)|(0.01)|(0.00)|(0.30)|(0.45)|
|Land Area,|-1.90|-5.22|-1.41|-0.26|-0.10|
|1,000,000 sq.|(0.24)|(0.16)|(0.04)|(0.00)|(0.52)|
|km.||||||
|Total|-0.03|0.33|0.04|0.02|0.01|
|population, 10<br>millions|(0.80)|(0.16)|(0.24)|(0.00)|(0.34)|
|Observations|1,067|1,251|1,611|1,365|1,365|
|R-squared|0.6|0.2|0.1|0.2|0.010|

Note: Results of ordinary least squares regressions of public policy and investment variables in country-years where per capita income of the observation is less than the median country per capita income that year.

Among poor countries, elections have no association with development outcomes.

The estimated coefficients for education are similar to those in Table 1, but standard errors are dramatically higher; poor countries with competitive elections do not exhibit significantly greater school enrollment than poor countries without. Competitive elections are again not associated with any differences in private investment. The estimated association of competitive elections on corruption and bureaucratic quality is near zero among poor
**Figure 6. PCA of Employment Protection from Doing Business and Level of Development**

<!-- Start of picture text --> HND PRY MEX PAN<br>PER<br>SUR<br>ECU<br>CHL<br>BHS<br>LCA<br>GUYGTM BLZ VCTDMA DOM CRIBRA ARG TTO<br>NIC SLV<br>URYATG<br>GRD BRB PRI<br>KNA<br>JAM COL<br>8 9 10 11 12<br>Log of GDP PC PPP<br>(p 50) lepl_db (p 50) lepl_db<br>Fitted values Fitted values<br>2<br>1.5<br>1<br>.5<br>Log DB measure of regulation<br>0<br>-.5<br><!-- End of picture text --> Source: Authors application of PCA to Doing Business Labor Market Regulation, and WDI

# **IV. Results**

Two sets of multivariate conditional correlations are presented in Tables 4 and 5. In Table 4 we present the estimates of how _de jure_ labor market policy variables relate with the extent of general (that is, household) use of digital technology ( _lint_usage_ ). In Table 5, we show how these estimates differ when the focus narrows on businesses’ use of digital technology ( _ldig_bus_pca_ ). For both general and business usage of digital technology, we show 17 specifications in each table. This allows full presentation of how the digital technology usage variables relate to: first, a set of key, contextual control variables; then to the published labor market regulation composite indices of the OECD-IADB and ILO; and different combinations of our PCA measures of employment protection from the Doing Business LMR indicators. The last specification at the right-hand side of Table 5 is our preferred.

In both tables, the relevant contextual control variables in logs, are: (i) GDP per capita ( _lgdppcppp_ ) to capture a country’s level of economic development; (ii) the rate of inflation ( _lwdi_inflation_ ) to account for economic stability and its effect on agents’ confidence to invest in new technology; (iii) the log of available physical infrastructure ( _linf_index_ ) capturing the importance of connectivity, transport and other physical infrastructure to the readiness of a country for broad-band roll-out and widespread 16
**<u>Table 2. Donor Altruism and Bureaucratic Quality: High-Aid Sample, 1982-2001</u>**

|Equation|1|2|3|4|
|---|---|---|---|---|
|Fragmentation variable|Ai|DA|D|AC|
|Constant|5.051<br>(1.557)|5.125<br>(1.507)|2.780<br>(1.264)|2.730<br>(1.191)|
|Initial bureaucratic quality|-1.120**<br>(0.206)|-1.111**<br>(0.198)|-0.973**<br>(0.172)|-0.979**<br>(0.160)|
|No. of years covered|-0.022|-0.032|-0.008|-0.002|
||(0.077)|(0.078)|(0.069)|(0.060)|
|Population change/ initial|0.248|0.276|0.535|0.514|
|population|(0.295)|(0.280)|(0.342)|(0.320)|
|GDP p.c. change/|0.155|0.148|0.138|0.143|
|Initial GDP p.c.|(0.134)|(0.132)|(0.111)|(0.106)|
|Aid/GNI|-0.033*<br>(0.014)|-0.032*<br>(0.013)|0.002<br>(0.019)|0.001<br>(0.018)|
|Fragmentation|-0.045**<br>(0.012)|-0.045**<br>(0.013)|-0.061**<br>(0.020)|-0.060**<br>(0.019)|
|UN share of aid|0.042<br>(0.047)||0.074*<br>(0.031)||
|IFI share of aid|0.029<br>(0.026)||0.082*<br>(0.032)||
|“Like-minded donor” share of|-0.020|-0.020|0.019|0.019|
|aid|(0.014)|(0.014)|(0.012)|(0.011)|
|Multilateral share of aid||0.033<br>(0.020)||0.079**<br>(0.024)|
|R<sup>2</sup>|.74|.74|.76|.76|
|Std. error of est.|0.96|0.94|0.92|0.90|
|Mean, dep. var.|-0.17|-0.17|0.17|-0.17|

Sample includes 30 countries with aid/GNI > 7 percent. Robust standard errors are in parentheses. A<sup>#</sup> , * and ** respectively indicate significance at .10, .05 and .01 level.

Equations 3 and 4 substitute the DAC-based for the AiDA-based fragmentation index. In equation 3, coefficients are positive for all three altruism variables, and are significant at the .05 level for the UN and IFI shares. As these two coefficients are very similar, they are added together in equation 4, where the multilateral aid share is significant at the .01 level. The like-minded donor coefficient approaches significance at the .10 level. Again, the addition of these altruism proxies substantially increases the magnitude and significance of the fragmentation coefficient, relative to equation 3 of Table 1.

In other tests not shown in the tables, project proliferation—as measured by project counts from the AiDA database—was not associated with changes in bureaucratic quality. This result holds whether or not one adjusts the project count totals for 23
me to use variation in conflict intensity over time that would be discarded in the traditional approach. In addition, the second approach provides the opportunity for useful checks of the robustness of my findings for potential migration and mortality biases.

I consider two outcome variables: a binary indicator for primary schooling completion and the number of years of education completed. Less than 45 percent (20 percent) of the male (female) adult population surveyed in the 2006 Nepal DHS had completed primary education, so primary schooling completion is a relevant cutoff in the present context. Given the recent occurrence of the conflict, a focus on primary education also has an advantage in that many individuals whose primary schooling careers coincided with the conflict period are old enough to have completed their primary education; hence, their long-term primary schooling outcomes are observed. Finally, given the high prevalence of voluntary migration in Nepal, it is important to test the robustness of my findings to migration bias. I do this by comparing the schooling attainment of children under 15 years of age surveyed in 2001 and 2006 in a given district. This age group is appropriate as long as I focus on primary education. As of 2004, 97 percent of Nepali migrants were men aged 15–44 years who typically left their wives and children behind (Lokshin and Glinskaya 2009). By focusing on children under 15 at the time of the survey, the individual is thus both unlikely to have migrated himself and unlikely to have accompanied a migrant parent. The DHS did not collect detailed migration data, but it does provide data on the date of arrival at the current location for women of reproductive age. Before the age of 15 years, the overwhelming majority of children are still living with their mothers; hence, I can further test the robustness of my findings for migration bias by restricting the sample to children whose mothers had not migrated since the beginning of the conflict.

**<<B>>** <u>Specification 1: Exploiting Differences in Exposure to Conflict between Birth Year</u>

<u>Cohorts within Districts</u> 11
21 CO is the most spatially sensitive emittant. FPA has specialized models for estimating CO concentrations within the vicinity of roadways. There are no dose-response functions available for CO that will provide health endpoints for valuation (although there is a one-hour ambient CO standard).

N',x has little, if any, effect on health in its own right, at least at ambient concentrations in U.S. cities. Its importance is through creating ozone. The same can be said for HC. Although some components of hydrocarbons emitted from vehicles are carcinogenic (such as benzene), risk assessments for these air toxics invariably show trivial effects on health of their elimination, let alone for the small differences in their concentrations that would be examined as part of an assessment of effects of alternative policy instruments.

Particulates are of potentially major concern, as a recent set of studies have shown consistent and significant effects of daily maximum particulate concentrations on mortality within an urban area (Schwartz and Dockery, 1991). As almost all of these studies feature linear doseresponse functions, annual average particulate concentrations can be used without hesitation.

Because there are so many industrial sources of particulates, to link changes in mobile source particulat **e** missions to concentrations requires an emissions inventory and ambient particulate readings; assuming proportionality between reductions in mobile source particulat **e** missions and particulate concentrations is probably acceptable, with one caveat. Some attention must be paid to particle sizes. Diesel particulate emissions are extremely fine, so a change in these emissions will not necessarily change total particulates proportionally.
– **Pooling** In principle, larger village infrastructure investments should produce more benefits than smaller ones will. Similarly, under a system of popular voting, communities that unite around a common project should be able to out-vote atomized groups that only vote for their own small program. However, we see no trends towards building larger projects or of proponent groups banding together to negotiate strategies. If anything, over time, social and political pressures to ensure that everybody gets something will grow stronger. While this is not an issue unique to CDD programs per se, it does raise challenges regarding economies of scale and the ability to plan in a more integrated fashion.

# **VIII. What Don’t We Know?**

**Sustainability of impacts.** As mentioned earlier, it will be important to conduct more evaluations looking at program impacts over time.  Impact evaluations covering longer time periods will also be important to assess the sustainability of welfare, social capital, and governance impacts. Unfortunately, the number of long-term impact evaluations is so small as to make any generalization premature. The longest panel survey we are aware of for a CDD program is the Philippines’ KALAHI-CIDSS program, which measures impacts over seven years.<sup>7</sup>

This dearth of longitudinal studies is in no way unique to CDD, but findings over time will certainly be valuable additions to our knowledge base. Positive impacts may attenuate when initial high returns from low baseline situations do not continue and investments yield diminishing returns. On the other hand, positive impacts could also increase as project actors become more used to the processes, and as repeated treatments add greater expertise in planning and managing resources. As many of these programs continue, it will be important to measure whether program impacts attenuate or change, and what design variations are feasible.

Because the majority of CDD projects are relatively new, little is known about how they evolve over time. As CDD ’ s critics of direct disbursements have noted, there is in principle an inherent tension between directly transferring funds to communities on the one hand, and relying on local governments that have the mandate and responsibility to manage public development investments. Over time, a maturing decentralized or deconcentrated local

> 7 The fourth wave of evaluation for Indonesia’s _PNPM Generasi_ program is forthcoming, measuring results over a nine-year time horizon (2007-2016).

24
As of today, however, few public registries are using their data in these more sophisticated ways. Only six of the 60 public registries surveyed indicated that the main use of PCR data in supervision was for provisioning<sup>24</sup> and even then, it was in the context of ensuring that problem loans were identified so that adequate provisions could be assigned, not for forward looking risk mitigation.

An important observation is that the typical structure of a PCR does not match Basel II’s minimum requirements for data for the IRB approach. Basel II suggests that there should be at least two dimensions for such data (i) on the risk of the borrower and (ii) on the instrument. Regarding the risk of the borrower, there should be at least 7 rating buckets or grades for loans that are not in default and at least one grade for loans in default. The proposals state that, “a borrower grade should be defined as an assessment of borrower risk on the basis of a specified and distinct set of rating criteria, from which PD (probability of default) estimates are derived.” Banks with loans concentrated in one a particular market segment should define rating grades such that loans are not unduly concentrated in a particular grade. The description of the ratings grades should be sufficiently detailed and clear to ensure loans of similar risks are classified in the same grades and that a third party can understand the assignment of borrower ratings. Rather than countries’ rushing to adapt their PCRs to this standardized description, we suggest here that PCRs may play two useful distinct and roles in terms of Basel II implementation.

First, and as suggested in a companion paper, for those countries applying the simpler Basel II approaches, the databases may be used to enhance forward looking provisioning such that capital plus provisions reflect an overall estimated Value at Risk. This implies setting capital requirements equal to, say, those given by the Standardized Approach (SA) and then setting provisions to equal the Value at Risk minus the SA capital requirement. This would allow countries with a low penetration by rating agencies (the majority of developing countries) to link banks’ reserving policies to risk but falling short of full IRB implementation.<sup>25</sup> The ratings would be those of the banks but as the rating scale would be centralized, and the database would not comply with the full Basel II requirements, this could not formally be referred to as an IRB

> 24 These six countries are: Brazil, Costa Rica, Dominican Republic, Madagascar, Peru and Vietnam. 25

> The standardized approach links banks’ capital requirements to the external credit rating of borrowers.

23
Table 12 (the first column reports baseline estimates). The main takeaways from the comparisons are that changing the definition of the age range of household heads has very little effect on the findings, while shifting the poverty line does make some difference in the configuration of poverty mobility. This is, in a way, expected, given that a 20 percent shift of the poverty line certainly qualifies as major; however, the overall picture that emerges from the results does not change drastically. In particular, the magnitude of chronic poverty is affected, but the country still appears as having more upward than downward mobility, with the vast majority of the population being unaffected by poverty.

**Table 12. Sensitivity of estimated joint transition probabilities to the poverty line and the age of household head**

||**PL**<br>**25<age<55**|**PL**<br>**25<age<65**|**PL-20**<br>**percent**<br>**25<age<55**|**PL-20**<br>**percent**<br>**25<age<65**|**PL+20**<br>**percent**<br>**25<age<55**|**PL+20**<br>**percent**<br>**25<age<65**|
|---|---|---|---|---|---|---|
|Poor - oor|5.8|6.7|2.3|2.8|11.0|12.4|
|p|(0.17)|(0.16)|(0.07)|(0.08)|(0.29)|(0.27)|
|Pr nnr|12.7|12.9|8.5|8.9|15.7|15.6|
|oo - o-poo|(0.37)|(0.35)|(0.28)|(0.26)|(0.41)|(0.39)|
|Non-oor - oor|3.0|2.5|1.5|1.2|4.8|3.9|
|p p|(0.07)|(0.05)|(0.04)|(0.03)|(0.1)|(0.08)|
|Non-poor - non-|78.5|77.9|87.7|87.2|68.6|68.0|
|poor|(0.54)|(0.49)|(0.36)|(0.33)|(0.69)|(0.62)|

Notes: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Standard errors in parenthesis.

# **5 Conclusions**

The paper applies a methodology recently developed by Dang and Lanjouw (2023), that delivers point estimates of poverty transition probabilities in the absence of longitudinal data. The analysis focuses on the dynamics of poverty in Bhutan between 2017 and 2022. This case study is interesting because the observed and documented reduction in poverty appeared particularly significant (the poverty headcount ratio was 28 percent in 2017, and only 11.5 percent in 2022) and, as recorded by the results obtained with the decomposition _à la_ Datt and Ravallion (1992), unusually little of this decrease is explained by the role of economic growth, but rather by redistributive effects. In light of this, an analysis based on synthetic panels can help identify the flows responsible for observed changes in poverty.

21
# **Table  2.  Need Factors Used for Grant Financing of Health Care in Selected Countries**

_Country name_ _Need Factors for Health Care Grants_ _(a) Need based top-up for health care in general grants_ Belgium Age, gender, unemployment, disability Finland (to local governments) Age, disability, remoteness, local tax base Germany Age, gender Netherlands Age, gender, urbanization, income base Switzerland Age, gender, region, income _(b) Need-based specific purpose transfers for core health services_ Denmark Age, children of single parents England Age, sex, mortality, unemployment, elderly living alone France Age Italy (two-thirds) Age, gender, mortality Northern Ireland Age, gender, mortality, low birth weight Norway (50 percent) Age, gender, mortality, elderly living alone Portugal (15 percent) Burden of illness: diabetes, hypertension, AIDS, tuberculosis Scotland Age, gender, mortality, rural costs Spain Cross-boundary flows Sweden Age, living alone, employment status, housing Wales Age, gender, mortality, rural costs _(c) Health transfers using composite indexes based upon principal component analysis_ Brazil Infant mortality, 1–64 mortality, 65+ mortality, mortality rate by infectious and parasitic diseases, mortality rate for neoplasia, mortality rate for cardiovascular conditions, adolescent mother percentage, illiteracy percentage, percentage of homes without sanitation, percentage of homes without running water, percentage of homes without garbage collection.

South Africa Percentage female; percentage children under 5; percentage living in rural area; percentage older than 25 without schooling; percentage unemployed; percentage living in traditional dwelling, shack or tent; percentage without piped water in house or on site; percentage without access to refuse disposal; percentage without access to phone; percentage without access to electricity; percentage living in household headed by a woman.

_Source_ : World Bank (2006).

7
**Step II:** ICAO’s own carbon footprint calculator provides passenger to freight factors and actual, average, passenger load factors, by region pairs, for 2008. These factors are used in order to adjust down the share of an aircraft’s fuel that is spent transporting passengers (as opposed to transporting freight). The calculations were refined to include these regional breakdowns. The load factors were applied evenly throughout all three classes of service. The sample size remains the same 157,297 flights as in Step I above. These represented a total passenger flight distance of roughly 341 million miles. Note that, in the first and second phases of the calculations, since these did not represent all HQ-booked air trips in that year, a factor of 1.31 was necessary for scaling up this number, to the set of total HQ-booked trips(as our sample represented about 76% of total flown distance on HQ-booked flights in that year).

**Step III:** Calculations under Steps I and II were based on an assumption that load factors are the same in all travel classes. More likely, as argued above, business and first classes have lower load factors than economy class. Since the even distribution of load factors could present an underestimating bias, calculations were made using alternative load factors by travel class, and a matrix developed showing this would affect the overall footprint.  The economy class load factor was adjusted to make the overall aircraft load factor match that of the regional load factors applied in Step II of the calculations. We unfortunately do not have direct observations of classspecific load factors (as such data are no longer reported by airlines, and are neither available from the ICAO); assumptions about these had to be made by us.

Since the airline/aircraft class configurations, extrapolated in Steps I and II above, could yield illogical or unreasonable results when applying this calculation methodology, only flights using verified configurations were used, reducing the sample size to 123,922 flights. This is thus a smaller subset of trips than those used under Steps I and II. The scaling-up factor, necessary to represent all HQ-booked air travel in CY-09, was correspondingly higher, 1.55.

**Table 5.1: Distribution of Trips Booked at HQ in CY-09, by Travel Class and Trip Length**

|**Class**|**Trips**|**Miles**|**Avg. Length**|**% of Miles**|
|---|---|---|---|---|
|**Coach**|53,535|87,442,266|1,643|19.5%|
|**Business**|124,516|329,138,314|2,323|73.6%|
|**First**|11,418|30,797,763|2,627|6.9%|
|**Total **|**189,469**|**447,378,343**|**2,361 **|**100.0%**|

Table 5.1 shows features of air trips booked through AMEX by HQ-based staff for CY-09 by travel class and average trip length, assuming that figures from our available sample can be scaled up proportionately for all these categories. We see that about 20% of trip length was traveled in coach class, 74% in business class, and 7% in first class. The relative frequency of first class travel in overall WBG travel was five times that for all air travelers (about 1.4 %). For 18