rafmacalaba/datause-encoder-data huggingface dataset:
**_Table 1: Summary of Correlations of Pricjs and NA Vs_**

||**Mean**|**Std. Dev.**|**Maxim**|**Minim**|
|---|---|---|---|---|
|CP'N|**0.4136**|**0.1780**|**0.7175**|**-0.039**|
|**CP,-**|I<br>**0.4525**|**Li. 1560**|**0.7941**|**0.2167**|
|**ICpsp**|**0.3297**|**0.i465**|**0.5680**|**-0.131**|
|CNL|**0.7851**|**0.1764**|**0.9784**|**0.1944**|
|CN,SP|**0.2143**|**0.1934**|**0.5468**|**-0.161**|

**Source: Table 8.**

These numbers are (across funds) averages of the correlation coefficients computed between two variables for each fund. Fc example, the correlation coefficients between prices and NAVs are computed first for each fund, resulting in a new series of thirty-two observations. Then a univariate exercise is done on that series, to obtain a mean of 0.4136, standard deviation of 0. 178, and so or, The same procedure is used for the other four pairs of coefficients.

The first coefficient (0.4136) reflects that returns on prices and on NAV have recorded some degree of co-movement over the period, in average terms. This provides thb. initial evidence of imperfect substitution between NIFs and their underlying assets (see Case III of the companion paper).

Secondly, country funds price returns are slightly more correlated with the home country index returns (average of 0.4525) than to S&P500 index returns (0.3297). Given that the typica!

8
# Appendix Table 3: Sizes of industries, 2000

|||Or|ganized sec|tor|Un|organized se|ctor|
|---|---|---|---|---|---|---|---|
|NIC|IndustryDescription|Plants|Empl|Output|Plants|Empl|Output|
|15|Food products and beverages|21.4|1,261.4|1,234,459|1,743.6|4,622.3|392,886|
|16|Tobacco products|2.2|475.2|103,597|169.7|485.8|16,820|
|17|Textiles|12.3|1,245.9|773,018|724.5|2,034.3|149,788|
|18|Wearing apparel; dressing and dyeing<br>of fur|2.8|329.2|141,507|2,018.0|3,257.1|93,687|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear|2.2|135.7|88,793|130.5|287.3|22,159|
|20|Wood and wood products, except<br>furniture; straw and plating|2.7|45.6|18,731|1,226.7|2,646.0|76,124|
|21|Paper and paper products|3.2|176.1|178,617|59.6|189.9|17,998|
|22|Publishing, printing and reproduction<br>of recorded media|3.0|116.7|53,516|109.4|382.3|36,042|
|23|Coke, refined petroleum and nuclear<br>fuel|0.8|66.7|771,868|5.6|19.0|2,807|
|24|Chemicals and chemical products|9.9|779.8|1,439,134|38.8|198.9|40,877|
|25|Rubber and plastic products|6.4|251.1|258,618|50.5|216.3|42,320|
|26|Other non-metallic mineral products|10.5|428.3|288,191|623.9|2,562.2|116,946|
|27|Basic metals|6.5|551.0|820,940|15.5|73.1|28,293|
|28|Fabricated metal products, except<br>machinery and equipment|7.9|292.6|181,534|282.1|797.0|80,883|
|29|Machinery and equipment, n.e.c.|8.9|422.9|373,526|52.5|224.1|43,967|
|30|Office, accounting and computing<br>machinery|0.2|17.1|36,757|0.2|0.9|309|
|31|Electrical machinery and apparatus,<br>n.e.c.|3.7|229.0|237,760|35.8|183.6|283,943|
|32|Radio, television, and communication<br>equipment and apparatus|1.0|109.1|166,577|4.0|25.7|2,995|
|33|Medical, precision and optical<br>instruments, watches and clocks|0.9|58.8|42,137|4.7|19.3|3,511|
|34|Motor vehicles, trailers and semi-<br>trailers|2.5|257.3|389,512|9.3|45.9|9,931|
|35|Other transport equipment|1.8|182.7|200,111|8.3|38.2|7,273|
|36|Furniture, manufacturing n.e.c.|2.1|119.3|105,705|435.0|1,078.1|90,704|
||Traditional|70.3|4,626.0|3,167,666|7,523.1|18,342.3|1,094,037|
||Modern|42.8|2,925.5|4,736,939|225.1|1,045.0|466,225|

Notes: Plants and employments are expressed in thousands. Output is expressed in millions of Rupees. "n.e.c." stands for Not Elsewhere Classified. Taken from Ghani et al. (2013).
To define the corridors used during the aggregation of OD‐level flows, a graphical depiction of the total road network was superimposed with a simple map of the highways, and roads classified as being either highways or regional roads. For the corridor aggregation, the major cities and the catchment areas surrounding the cities were identified as the core catchment due to the high densities involved, while outlying areas were identified as the wider catchment due to lower densities: flows from a core to a core were defined as corridor Class 1 and are flows from a single origin to a single destination, flows from a core to a wider catchment areas are defined as corridor Class 2, and flows from wider catchment areas to wider catchment areas are defined as corridor Class 3. The difference between Class 3 and the ‘other’ typology is that Class 3 flows still flow along the corridor and can therefore be easily consolidated with other corridor flows.

# **<u>Modeling Transport Costs</u>**

With a complete database of freight flows, transport costs are calculated in a similarly disaggregated fashion. Actual rail and waterway tariff data were received from the respective operators. Road tariffs (costs) were calculated as per equation (8).

<!-- Start of picture text --> L �∑ni�1 ∑pj�1 ∑sk�1 xyijk �� d �c �l �q �e �f �m �z�ijk �tk� (8)<br>L = road line haul cost d = depreciation rate per tonne‐km<br>n = number of commodity groups c = cost of capital per tonne‐km<br>i = commodity grouping l = license fee and road tax per tonne‐km<br>p = number of typologies q = insurance per tonne‐km<br>j = typology e = driver fees per tonne‐km<br>s = number of routes f = fuel cost per tonne‐km<br>k = route m = maintenance and repair costs per tonne‐km<br>x = tonnes transported z = tire wear cost per tonne‐km<br>y = distance in kilometers t = toll fees per tonne‐km<br><!-- End of picture text --> This equation involves the summation of all the different cost elements of road transport within a typology on a specific route. The different cost elements of road transport are determined by vehicle type; this, in turn, is determined by the commodity type, typology and route of travel. The commodity’s ‘preferred’ vehicle type will change with changes in each of these variables. Once the vehicle type and volume are known, the cost elements can be assigned according to equation (8), above. The core drivers of transport costs, i.e. weight in tonnes (x) and distance traveled (y), describe the base of the formula.

It is however important for logistics infrastructure and efficiency planning and will be modeled based on freight flows developed from truck counts in subsequent phases of the project.

26
To better understand whether, when, and how the World Bank effectuates development policy change, we leverage new data on the organization’s lending and non-lending operations as well as a unique survey of 1,328 public officials that measures the policy influence of the World Bank in 24 issue areas and 113 developing countries. We hypothesize that the World Bank’s non-lending instruments are more effective than its lending instruments at influencing the policy priorities of client countries. Our empirical results not only provide support for this hypothesis, but also demonstrate that World Bank AAA products influence the design and implementation of government policy.

# **Theory and Hypotheses**

## _Role of Incentives_

The World Bank is a lending institution that depends on governments who are willing to borrow funds and repay their debts. These borrowers have access to two different types of loans: investment loans for stand-alone projects and development policy loans (DPLs) that do not support stand-alone projects but instead provide budget support in exchange for policy reform.<sup>3</sup> The conventional wisdom is that if the World Bank has any influence on the nature, speed, sequencing, and extent of policy reform in developing countries, it is primarily through its DPL program (Smets and Knack 2016, 2018; Giordano and Pagano 2017). A key feature of this program is the fact that financial disbursements are linked to the adoption of specific policy conditions. The World Bank refers to these conditions as “prior actions” (PAs), and its ability to withhold disbursements—or threaten to withhold disbursements—from borrower governments is a major source of bargaining power.

> rectives.” Similarly, in a review of health care and pension reforms undertaken in Latin America during the 1980s and 1990s, Weyland (2006: 10) finds that “the IFIs most powerful instrument of coercion, loan conditionality, has proven to be a blunt weapon in the enactment of complex institutional reforms. Reforms of social security and health systems involve a wide range of actors. This multiplicity of veto players and the resulting need for political negotiations make it difficult for external actors that lack democratic legitimation to exert much

> 3During the 1980s and 1990s, DPLs were known as structural adjustment loans (SALs).

3
<!-- Start of picture text --> Kernel density estimate Kernel density estimate<br>o<br>2 be<br>c=]<br>8<br>c=<br>aa<br>5 c=<br>o o<br>20 40 DIF cos ey ies ) 20 DTF40 Enforcing contracts60 80 100<br>— 7005 —— 2003<br>—— 3013 —— 2013<br>— ikov, bandwicth = 4.0842 kernel = epanechnikov, bandwidth = 4.5472<br>2 Kernel density estimate Kernel density estimate<br>o: :2#<br>is3|<br>8<br>My<br>zo =<br>Z ag<br>as a<br>Ss7 =i<br>=) =)<br>i) 20 OTF40 ina | 60 80 100 0 20 40 60 80 100<br>Protecting investors DTF dealing with construction permits<br>2013<br>kemel = epanechnikov, bandwidth = 4.7550 kemel = epanechnikov, bandwidth = 5.6471<br><!-- End of picture text -->
notion that groups or societies think in a kind of mystical, quasi-Hegelian fashion. Here one imagines a thick culture that, like an ether, pervades and determines history. Second, some analysts (particularly methodological individualists using surveys to get at meanings and identities), take the view that culture inheres within the minds of individuals as an essence. Methodological individualists typically look for correlations between self-reported beliefs and behavior at the individual level (e.g., are members of a particular religion more likely to exhibit altruism?). In contrast to these views, we want to emphasize an account in which intersubjective meanings are a necessary consequence of the structure of meaning _events_ , which require a shared background – or what Taylor (2003) calls ‘social imaginaries’ – to take the form that they do. When individuals make political, social, and economic choices, their expression takes the meaning it does because the individuals themselves – _as members of a community or communities_ – know the social background, and those who interpret their choices are confident of the meanings they ascribe to the choices because they know that the individuals who have acted are acting against the shared social background. As Taylor (1985: 40) famously puts it, “What the ontology of mainstream social science lacks is the notion of meaning as not simply for an individual subject; of a subject who can be a ‘we’ as well as an ‘I’.” Because many analysts view shared meanings as cultural essences rather than interpretations of coordinated social practice, there is a tendency to emphasize their role in the construction of ethnicities, group boundaries and identities. But it is important to emphasize that intersubjective meanings involve much more than group identities. Rather, they are constitutive for the full variety of spheres in which coordinated social action occurs, including social relations, politics and governance, economics and exchange, and spiritual practice. In Indonesia, for example, the practice of deliberation leading to a social agreement is called _musywarah_ . Most notably for our purposes, _musywarah_ is not mere bargaining, in which various private interests are weighed against each other on the basis of (more or less equal) positions of leverage and using (more or less fair) processes of aggregation. Rather, _musywarah_ is a process of deliberation regarding private concerns that engenders its own legitimacy; in fact, in some accounts, it is an alternative to voting.<sup>15</sup> This language for the description of deliberation in Indonesia enables an experience of organicism in society more difficult to achieve in, say, England, but disables the widespread use of court-based adjudication.

> 15 See Gibson and Woolcock (2008) for a discussion of the role of development projects in shaping the terms of public deliberation regarding resource allocation and community disputes between high and low status groups in rural Indonesia.

10
Table 4: Estimations of industry traits associated with increased entry of new women-owned establishments

||<br>DV is|<br>log count of ne<br>Industrytra|<br>w women-owned<br>it used in interact|<br>establishments i<br>ion,calculated i|<br>n unorganized s<br>n 1994 at the na|<br>ector by state-i<br>tional level:|<br>ndustry|
|---|---|---|---|---|---|---|---|
||Log share of|Log share of|Log share of|Log average|Log average|Log average|Log average|
||<br>unorganized<br>establishments|<br>unorganized<br>establishments|<br> <br>unorganized<br>establishments|<br>establishment<br>size|<br>value-added<br>per worker|<br>fixed capital<br>per worker|<br>financial<br>liabilities as a|
||owned by<br>women<br>|that are<br>household-<br>based<br>|that are<br>household-<br>based and<br>women-owned<br>||||share of output<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||A|. Base estimatio|n|||
|(0,1) state has political reservations|0.332++|0.224+|0.399+++|-0.291++|-0.192|-0.280+|0.110|
|x industry trait in column header|(0.129)|(0.121)|(0.135)|(0.133)|(0.146)|(0.151)|(0.092)|
|Observations|3606|3606|3606|3606|3606|3606|3606|
|Adjusted R-squared|0.737|0.736|0.738|0.735|0.735|0.735|0.736|
|<br>Controls||State|x industry, State x|year, and Indus|try x year fixed|effects||
|||B. Panel|A including male|analogue of acti|vity in state-ind|ustry-year||
|(0,1) state has political reservations|0.335+++|0.211+|0.390+++|-0.263++|-0.184|-0.277+|0.108|
|x industry trait in column header|(0.128)|(0.119)|(0.134)|(0.132)|(0.145)|(0.150)|(0.091)|
|DV analogue for male-owned|0.098+++|0.096+++|0.096+++|0.105+++|0.107+++|0.107+++|0.097+++|
|establishments in state-industry-year|(0.031)|(0.031)|(0.031)|(0.036)|(0.036)|(0.036)|(0.031)|
|Observations|3606|3606|3606|3606|3606|3606|3606|
|Adjusted R-squared|0.740|0.739|0.740|0.738|0.738|0.738|0.739|
|<br>Controls||State|x industry, State x|year, and Indus|try x year fixed|effects||

Notes: Regressions quantify industry traits associated with adjustments in women's entrepreneurship in India surrounding the implementation of state-level political reservations for women. The dependent variable is log count of new women-owned establishments in the unorganized sector by state-industry. Regressions contain 3606 state-industry observations and include state x industry, industry x year, and state x year fixed effects. Panel A includes a modified indicator variable for the period after a state implements the political reservation interacted with the industry trait from 1994 given in the column header. The modification allows for fractional values in two states that had pre-existing set-asides that were below the level mandated by the Amendments. Fixed effects absorb the main effects of the interactions. Industry traits are expressed in logarithms. Panel B further includes a control for the analogue of the dependent variable for male-owned establishments. Regressions are weighted by log initial employment in state-industry in 1994 and cluster standard errors by state-industry. +++, ++, and + indicate statistical significance at a 1%, 5%, and 10% level, respectively.
5

**Table 2. Main Labor Market Indicators (in %)**

||Share of the working<br>age population|Labor force<br>participation|Employment-to-<br>population ratio|Unemployment<br>Rate|Relaxed<br>unemploymentrate*|Relaxed labor force<br>participation*|
|---|---|---|---|---|---|---|
|_All_||49.3|42.1|14.7|25.3|56.3|
|_Gender_|||||||
|Male|46.8|73.4|65|11.4|18.4|79.7|
|Female|53.2|28|21.8|22.3|38.7|35.6|
|_Living area_|||||||
|Rural|57.1|49.8|42.7|14.2|23.1|55.6|
|Urban|42.9|48.6|41.2|15.3|28.1|57.2|
|_Age group_|||||||
|15-24|36.4|32.7|21.6|33.8|52.4|45.5|
|25-34|22.5|52.5|43|18.1|27.4|59.2|
|25-54|33.5|63|60|4.9|8.4|65.4|
|55-64|7.6|56|54.9|1.9|3.7|57|
|_Poverty_|||||||
|Non poor|55.9|48.7|42.5|12.6|23.6|55.6|
|Poor|44.1|50.1|41.5|17.2|27.4|57.1|
|Extremely poor|26.6|51|40.9|19.8|29.1|57.6|
|_Male by age group_|||||||
|15-24|17.2|45|31.3|30.3|45.4|57.3|
|25-34|9|85.6|74.6|12.8|18.3|91.4|
|25-54|16.3|94.5|91|3.7|5.4|96.2|
|55-64|4.3|78.6|77.4|1.5|3.3|80|
|_Female by age group_|||||||
|15-24|19.2|21.6|12.9|40.4|63|34.8|
|25-34|13.5|30.3|21.8|28.2|42.2|37.7|
|25-54|17.2|33.4|30.6|8.1|16|36.5|
|55-64|3.3|26.5|25.6|3.4|5.2|27|
|_Male by living area_|||||||
|Rural|26.4|71.3|68.8|10.5|15.9|68|
|Urban|20.4|67.7|60|12.7|21.9|64.2|
|_Female by living area_|||||||
|Rural|30.7|28.6|20.3|23.3|38.4|32|
|Urban|22.5|32.3|23.9|20.8|39.2|35.8|

Source: EPCV 2004. Figures are displayed in percentage. * The relaxed definition for unemployment includes all the unemployed according to the ILO definition as well as all individuals who did not work in the reference week and did not look for a job because they thought there were no jobs available.
Table 3: PM2 _._ 5 and children’s health

||Cough and<br>short breath<br>(1)|Fever<br>(2)|Diarrhea<br>(3)|Anemia<br>(4)|
|---|---|---|---|---|
|Panel A|||||
|PM 2.5 above|0.093**|0.039|0.025|-0.001|
|35 mg/m3|(0.047)|(0.054)|(0.034)|(0.074)|
|Observations|712|712|712|492|
|R-squared|0.053|0.060|0.067|0.238|
|Panel B|||||
|ln(PM 2.5)|0.073*|0.008|0.007|-0.054|
||(0.042)|(0.043)|(0.035)|(0.056)|
|Observations|712|712|712|492|
|R-squared|0.053|0.060|0.067|0.240|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the survey block level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions are estimated using OLS and include month and year fixed effects, individual controls (age and gender of child, mother’s age, indicators of household wealth, indicator for a smoking mother), and logs of monthly temperature and humidity. PM 2.5 refers to average PM2 _._ 5 in weeks _t −_ 1 and _t −_ 2, where _t_ is the period of 7 days prior to interview. Note that reference period for morbidity questions is weeks _t_ and _t −_ 1. Panel A uses an indicator of PM 2.5 being above the U.S. standard, while Panel uses log of PM 2.5.

17
Note, finally, that it is impossible to disentangle age, cohort, and year effects wih a single cross-section of data. Consider, for example, the ratio of the slopes of the earnings function for workers with university education relative to workers with primary education. This ratio appears to be noticeably larger in the graphs in the second panel of Figure 4 than those in the first panel. This difference in the relative slopes could be the result of an improvement in the quality of primary school which has affected recent labor market entrants-a cohort effect-or the fact that the relatively low returns to primary school only bite later on in a worker's life-an age effect.<sup>23</sup>

# **6. Conclusion**

There is considerable evidence which suggests an increase in the mean rate of return to education over time, increasing convexity in the earnings function, and large, significant sheepskin effects in education in the United States. Similar work on LDC's is still far from the norm-even though the supply-demand mismatches, and the poor information flows in the labor market which are (some of the possible) explanations for non-linearities in the earnings function are likely to be much more serious problems in these countries than in the United States.

This paper adds to the literature by exploiting a recently-available, high-quality data set, the 1998 Philippines APIS survey. I show that there are large differences in the wage premia across education levels, and very large, significant sheepskin effects

> 23 Farber and Gibbons (1996) present recent evidence for the United States which suggests that the effect of education on log wages diminishes with experience, although it is not clear how the _relative_ earnings of workers with different amounts of education change with experience.

23
Figure 22. Average annual total wealth per capita by period <!-- Start of picture text --> 90<br>78<br> 80<br>67<br> 70<br> 60<br>50<br> 50<br> 40<br> 30 26 27<br>21 21 22<br>18<br> 20<br> 10<br> -<br>Pre-boom Boom Post-boom<br>RR nonRR RR RoW<br>Thousand US$<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012). Values expressed in thousand constant 2018 US$. Source: Changing Wealth of Nations 2021.

We find that during the boom decade the total wealth stocks in resource-rich countries in Africa generally increased. This means, including price effects, the total value of a nations stock of human wealth, productive capital, and natural assets, rose. Figure 23 plots this effect for African countries expressed in per capita terms. Countries like Namibia and Botswana added over $20,000 per person to their total national wealth during this period.

33
<!-- Start of picture text --> E. Government revenue, by population size,<br>2011-23<br>Percent of GDP<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Micro states Other small states<br><!-- End of picture text -->

**F. Volatility of government revenue, 2000-23**

<!-- Start of picture text --> Standard deviation<br>10<br>8<br>6<br>4<br>2<br>0<br>exporters importers<br>Small states Small states: Commodity Small states: Commodity EMDEs excl. small states<br><!-- End of picture text --> _Sources_ : GFS (database); WEO (database); World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A. Panel shows average revenue to GDP ratios in 32 small states and 110 other EMDEs.

B. Bars show the average revenue to GDP ratios for the period 2011-22. Small states include 34 economies, and EMDEs excluding small states include 97 economies.

C.-E. Bars show the average revenue to GDP ratios for the period 2011-23.

C. EAP small states include 13 economies, LAC small states include 11 economies, and other small states include 11 economies.

D. Small states commodity exporters include 13 economies, small states commodity importers include 22 economies, and tourism-reliant small states include 22 economies.

E. Micro states have a population of less than 200,000. Micro states include 14 economies, and other small states include 21 economies.

F. Standard deviation of annual change in revenue to GDP ratios in 35 small states and 113 other EMDEs for the period 2000-23. Small states commodity exporters include 13 economies, and small states commodity importers include 22 economies.

38