rafmacalaba/datause-encoder-data huggingface dataset:
**Annex ‐ Table 5 ‐ SSA: Fiscal Regression for Model D**

|**_Dependent variable_**|||**Real GDP**|**per capita**|||||||**Real**|**GDPper c**|**apita**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||**Central**<br>**African**|**Cote**||||||||||
||**Benin**|**Botswana**|**Cameroon**|**Rep. **|**d'Ivoire**|**Ethiopia**|**Gabon**|**Gambia**|**Ghana**|**Kenya**|**Mozambique**|**Nigeria**|**Rwanda**|**Senegal**|
||_Column 1_|_Column 2_|<br>_Column 3_|_Column 4_|_Column 5 _|_Column 6 _|_Column 7_|_Column 8_|_Column 9 _|_Column 10_|<br>_Column 11_|_Column 12_|_Column 1_|_3 Column 14_|
|Constant|6.447<br>(1.896)*|29.834<br>(3.204)**|8.877<br>* (14.707)**|22.05<br>*(10.544)*|14.125<br>**(8.006)***|4.874<br> (3.072)***|27.915<br> (2.778)**|7.79<br> (7.323)**|2.845<br>* (7.259)***|6.69<br> (2.207)**|‐10.88<br>(‐2.389)**|0.284<br>(0.033)|7.129<br>(2.341)**|11.305<br>(9.486)***|
|Government revenue per capita|||||||||||||||
|(in constant US$)|‐0.031<br>(‐0.515)|0.009<br>(0.258)|0.037<br>(1.706)|0.058<br>(1.034)|0.119<br>(2.96)***|0.254<br> (3.908)***|0.198<br> (3.025)**|0.016<br>* (0.228)|0.03<br>(2.307)**|0.06<br> (1.167)|0.206<br>(3.007)***|‐0.012<br>(‐0.304)|0.2<br>(7.158)***|0.218<br> (2.043)*|
|Net lending per capita (in|||||||||||||||
|constant US$)|0.001<br>(1.744)*|0<br>(‐0.222)|‐0.005<br>(‐6.257)***|0.001<br>(0.627)|0<br>(0.558)|0.001<br>(0.406)|0.002<br>(0.963)|‐0.002<br>(‐0.955)|‐0.001<br>(‐3.844)**|0.002<br>*(1.421)|‐0.001<br>(‐0.925)|‐0.008<br>(‐1.564)|‐0.002<br>(‐0.787)|‐0.003<br>(‐2.981)***|
|Private investment (in GDP)|0.059<br>(3.201)***|0.132<br> (3.325)**|0.338<br>* (10.646)**|0.048<br>*(0.897)|0.038<br>(2.967)***|0.011<br> (1.414)|‐0.09<br>(‐1.274)|0.113<br>(1.795)*|0.003<br>(0.486)|0.036<br>(1.089)|0.01<br>(1.091)|0.064<br>(1.482)|0.027<br>(0.556)|0.016<br>(0.374)|
|Log of labor force|0.385<br>(1.615)|‐1.27<br>(‐2.076)*|‐0.001<br>(‐0.016)|‐0.767<br>(‐5.848)**|‐0.185<br>*(‐2.014)*|0.041<br>(0.432)|‐1.093<br>(‐1.473)|‐0.023<br>(‐0.297)|0.686<br>(20.521)**|0.176<br>*(1.024)|1.447<br>(4.667)***|0.574<br>(1.17)|0.141<br>(0.677)|‐0.078<br>(‐0.86)|
|AR(1)|0.862<br>(11.898)**|0.983<br>* (80.13)**|0.468<br>* (2.468)**|0.629<br>(1.785)*|0.48<br>(2.02)*|0.173<br>(0.636)|0.904<br>(10.401)*|0.222<br>**(0.846)|‐0.095<br>(‐0.405)|0.798<br>(3.375)***|0.675<br> (3.31)***|0.754<br>(3.983)***|0.893<br> (8.077)***|0.642<br> (2.386)**|
|MA(1)|0.171<br>(0.706)|0.964<br>(24.943)*|‐0.997<br>**(‐7.778)***|‐0.151<br>(‐0.323)|0.52<br>(2.118)**|0.93<br> (18.604)**|‐0.997<br>*(‐7.946)**|0.277<br>*(0.764)|0.94<br>(13.751)**|0.411<br>*(1.644)|0.109<br>(0.327)|0.568<br>(3.007)***|‐0.098<br> (‐0.363)|‐0.1<br>(‐0.277)|
|**Adjusted R2**|0.953017|0.998394|0.986517|0.935391|0.949683|0.830553|0.817665|0.601597|0.997503|0.782129|0.97047|0.840898|0.878395|0.842074|

The estimation technique is ARIMA. t‐statistics are given in paranthesis.*** stands for 1% significance level, ** 5% significance level, and * 10% significance level. AR(1) is autoregressive variable. MA(1) is moving average variable.

31
Table 5. Allocating management responsibilities to units with the highest stakes

|**Tier**|**Main risk**|**Process**|**Decides**|**Concurs**|
|---|---|---|---|---|
|**Flagships**<br>**Debate**|**World Bank’s reputation**|**Yes**|**Managing Director**<br>**Vice-President**|**Chief Economist**<br>**or delegate**|
|**Extended core**<br>**Other country**|**Client relationship**|**(ADM)**|**Country Director**|**Regional Chief**<br>**Economist**|
|**Research**|**Technical quality**||**Academia**|**No**|
|**Capacity**<br>**Internal**|**Resource waste**|**No**<br>**(Agile)**|**Budget holder**|**(Agile)**|

Finally, a third recommendation is to conduct more regular assessments of the quality and impact of analytical work across the entire institution, along the lines of what was done in this paper. Without aiming to provide a judgment on any individual knowledge product, data-driven studies of this sort allow assessing the overall performance distribution, and to identify the correlates of success. They would also allow to distinguish performance across various dimensions, such as technical quality, external visibility, and perceptions by end-users. This information, in turn, could be used to periodically adjust and upgrade knowledge management across the entire institution.

30
-3-

Sections IV and V. The results indicate a significant positive relation between the availability of subsidized loans and corporate speculative investment. Section VI contains concluding remaiks.

# H. CORPORATE DEBT SUBSIDIES AND INVESTMENT BEHAVIOR

# 1. **Debt Subsidy**

The most obvious forn of debt subsidy is the provision of funds at below market interest rates. More subtle implicit subsidies arise when there are: (i) official ceilings on interest rates and loan guarantee fees and (ii) bail-outs of companies in fnancial distress. Effective ceilings on interest rates and loan guarantee fees create excess demand for credit and lead to rationing. If for some reason (e.g., persuasion by governmental agencies) banks are required to extend or guarantee loans to high risk finns within the ceilings, the firms that are fortunate enough to obtain such loans or guarantees receive an implicit debt subsidy.

To illustrate, consider a bank that must eam a ten percent return on its loans to break even. The bank is contemplating a loan request from a firn that has a five percent probability of default, with twenty percent of the loan recoverable in the event of default. To break even the bank must charge at least [(1 + .1) - (.2)(.05)]A/1 - .05) - 1 = 14.7%.4 If this rate exceeds the interest rate ceiling, the optimal response for the bank is to deny the loan request. Suppose, however, tuit the bank is required to extend the loan and that the maximum rate it can charge is only 11.5 percent.<sup>5</sup> At this pmise rate, the rate the bank eRt to receive is (1 + .115)(.95) + (.2)(.0S) - 1 = 4Let

**A** R = the promised interest rate that fully reflects the default risk of the borrower; p = the probability of default;

d = the percentage of the loan recoverable in the event of defimlt; R = the break-even return on the bank's loan portfolio.

Then the promised rate R that the bank must charge to break even is: R = (1I + R) -p * d]/(l - p) - 1.

5The 11.5% used in this examplc was the actual interest rate ceiling on Korean bank loans during the mid 1980s, which is the sample period used for our empirical tests.
# _AiWb Cxkvwfy4L Sauduuk Sai*_ c _OGi&Wd The_ E _of Q_

# CountrY of Orkin

# Percentage of Total in Host Country

|_County_|_Toalin 50_<br>_Sekcted_<br>_Cowsa_|_US._<br>_1984_|_France_<br>_198_|_U.K_<br>_1983_|
|---|---|---|---|---|
|Al%ia|13,506|49|789|3.7|
|Egypt|13,35|154|-|44|
|ibpu|2,557|4D.1|-|25.7|
|Moroooo|31,464|z1|779|-|
|Tunisi-|10,860|5.1|75.1|-|
|Bahrain|1,811|_2.5_|-|14.8|
|Yemen,**FDR**|2,188||||
|Iaq|7,427|17.8|-|21.0|
|Jordan|24,48D|23.6||2.0|
|Kuwak|4,438|76.7||_55_|
|Lebanon|14,225|41.7|28.6||
|Oman|1,82|25.0|||
|Qatar|_1,051_|65.8||_5.7_|
|Saudi Aabia|8,302|799|-|-|
|Syra|_15I,681_|119|165|-|
|**U.A**|1,584|69.9|-|12.3|
|Yemen|2,431|18S5|||

**So= UNESCO** _as*I YwTlwk,_ 19e6

_52_
18

# **Figure 7. Trade barriers faced by CPTPP members before and after trade liberalization within the block, trade weighted averages in %**

<!-- Start of picture text --> CPTPP, Tariffs faced CPTPP, NTBs faced<br>Vietnam<br>Singapore<br>New Zealand<br>Malaysia<br>Japan<br>Brunei Darussalam<br>Australia<br>Peru<br>Mexico<br>2015 2030<br>Chile<br>Canada<br>0 1 2 3 4 5 0 5 10 15 20<br><!-- End of picture text --> Source: Authors’ estimates

**Figure 8. Trade barriers faced by TPP12 members before and after trade liberalization within the block, trade weighted averages in %**

<!-- Start of picture text --> TPP12, Tariffs faced TPP12, NTBs faced<br>USA<br>Vietnam<br>Singapore<br>New Zealand<br>Malaysia<br>Japan<br>Brunei Darussalam<br>Australia<br>Peru<br>Mexico<br>Chile<br>Canada 2015 2030<br>0 1 2 3 4 5 0 5 10 15 20<br><!-- End of picture text --> Source: Authors’ estimates
of the technical efficiency, also accepting that the distribution of the technical efficiency of a firm and the regressors (inputs) are independent. This assumption is very restrictive because it is reasonable to think that if the firm knows its inefficiency level, the selected quantities of the inputs can be affected. The statistical analysis of econometric models with panel data allows applications to the estimations of frontier models to be developed and, with them, one can partially solve the estimation problems<sup>2</sup> .

Schmidt and Sickles (1984) established the basic idea and Comwell extended it in 1990 (Comwell et al., 1990). The model suggested was as follows: if we have a data panel composed by T temporal observations for N productive units, we can represent the technology with the following production function (we assume a linear technology for simplicity): _K yj, =aoc+ I xki,C,k+ ej,, i = N; ,.......... t =1 k .. =1 ,T; (1).. ,K_ **_k=1_** where

**_-'i =_ )** **_- u,_**

where y denotes the output, **Xk** represents the kth inputs and Pk stands for the output elasticity with respect to the kth input. Finally, s is a composed error terms; **jit** is a disturbance term with the usual characteristics [iid, N(0,&2,)] that captures the random factors that can explain the divergence between the observed and the potential output enumerated above and ui captures the time-invaring latent individual effects. Then, the ui's are positive and iid with mean **t** and variance o2u and they are independent of uit. That is, [ui % D(gL, c&2u)].

> Therefore the parameter **t** represents the latent average inefficiency level of technology. We can also estimate (or not) a particular distribution for ui, and we can assume (or not) that inefficiency is correlated with the inputs. The technical efficiency measurement of an ith firm will be obtained from

_ET = e-u_ This model is a simple generalization of the stochastic frontier models corresponding to the usual literature of panel data models with individual effects. The only difference with the standard panel data models is that in Equation 1 the individual effects (u;) are one-sided.

> **_2_** Simar's article (1992) constitutes a good survey of the frontier methodology with panel data, with an

> application of the different methods and estimators proposed.

8
The partial evidence set out above suggests that in the absence of an explicit incomes policy in 1993, we observe quite sharp variation in wage settlements, whether discriminating in terms of firm size, ownership or other attributes. The picture is not one of generalized wage convergence to amounced inflation. Yet we also know that average industrial wage increases over 1993 were close to consumer prices. The distribution given in Table 14 probably hints at the reason. While a large number of firms indeed had small nominal wage changes, a significant share also harvested large increases, considerably in excess of wage guidelines or amnounced inflation.

Part of the reason for this polarity in wage settlements may be that guidelines are set and then incorporated in either branch or firm level agreements, in a good part of the remaining state sector as well as part of the privatized sector. Further, these settlements are made with reference to consumer prices; the wedge ensures that the burden is shifted to firms, if and when management's bargaining power is attenuated.

The wedge effect remains powerful because of a combination of factors. Reductions in consumer subsidies, food price shocks attributable to the drought, changes to VAT brackets and - rate in 1993 and the rise in the relative price of services given that the share of non-traded goods is higher in the consumer rather than producer price index and that consumption of non- traded goods is converging to OECD levels have been the main factors explaining the presence and size of the wedge L8* At first approximation, we can think of Hungarian workers and unions aiming at a stable consumption wage. Real wage resistance will obviously arise when fens' labor costs rise in response to exogenous changes which have an impact on workers. Wage bargains conditioned on consumer prices will obviously sanction the gap between the real labor costs of firms and the real consumption wage of workers. Further, the size of the wedge will be affected not only by the consumer to producer price relationship, but also by relative tax rates on employers and workers. Labor taxes paid by employers in Hungary are higher than OECD averages. However, changes to relative prices rather than to relative tax rates have primarily shifted the wedge in recent years. Real wage resistance has arisen because real labor costs have responded positively

> * Note tLat the wedge is significant for all industrial branches, the ppi range being 6-13% in 1993.

30
# **Appendix C: Relationship between PINFRA, SINFRA, PINFRA and its determinants**

<!-- Start of picture text --> 4.5 5 5.5 6 6.5 0 5 10 15<br>GSDP CEXP<br>PINFRA Fitted values PINFRA Fitted values<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> 0 10 20 30 0 20 40 60 80 100<br>SEXP INV<br>PINFRA Fitted values PINFRA Fitted values<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br><!-- End of picture text --> <!-- Start of picture text -->
-15 -10 -5 0 5 0 20 40 60 80<br>FD DEBT<br>PINFRA Fitted values PINFRA Fitted values<br>2<br>2<br>1.5<br>1.5<br>1<br>1<br>.5 .5<br>0 0<br><!-- End of picture text -->

<!-- Start of picture text --> 0 20 40 60 80 0 500 1000 1500<br>UR PDEN<br>PINFRA Fitted values PINFRA Fitted values<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br><!-- End of picture text --> 37
granddaughters when the grandmother is the beneficiary (Duflo 2003). These results show the type of ‘arbitrage’ that can be observed in an extended family. The role played by women is also striking. These minimum pensions have positive spillovers for other members of the extended family. Carvalho Filho (2008) has studied the effect on labor outcomes and school enrollment of children residing with the beneficiaries of the pensions awarded to rural workers in Brazil. These old age benefits foster school enrollment and decrease child labor participation, and the intensity of these effects depends on the gender of the beneficiaries and of the children concerned.

# **Simulation Results for 18 Latin American Countries**

We now turn to the results of our “mechanical” (as opposed to behavioral) microsimulations using household survey data for 18 Latin America countries for the latest available year. The surveys give us the disposable income (resources) and the family structure for aged people. We use two alternatives definitions of old age: 60 and 65 (Results for the latter are reported in the appendix). The disposable income is the sum of all the resources available in the family unit to which the elderly person(s) belong(s) divided by the equivalence scale. The simulation consists in introducing a minimum pension equal to the poverty rate. This pension is given to all elderly provided they do not receive any other transfers from the government. If they do, the new pension is adjusted accordingly. We consider two scheme depending on whether or not the minimum pension is means tested, the means being the resources of the elderly concerned.

We use two specifications for the minimum pension US$2.5 a day or 50% of the median income. We are interested in measuring the impact on the poverty rate and the 20
# **IV. Results**

# _A. Program impacts_

Table 3 reports estimated mean impacts on school enrollment (Columns 1–3), exam participation rates (Columns 4–6), and student exam scores (Columns 7–9) in each of the three program years.

First, with respect to school enrollment, mean impacts of the treatment variants either turn from negative to positive or are positive and grow in size over the three years. Third-year mean impacts are 3.9 students for _HT only_ , 2.5 students for _all T_ , and 5.8 students for _HT+_ , and are not statistically different from each other. However, only the mean impact for _HT only_ is significant. The pooled treatment has a significant mean impact of 4.1 students (baseline mean enrollment is 100 students).

Second, with respect to exam participation rates, mean impacts of the treatment variants also either turn from negative to positive or are positive and grow in size over the three years. Third-year mean impacts are 3.4 ppts for _HT only_ , 2.8 ppts for _all T_ , and 3.6 ppts for _HT+_ , and are not statistically different from each other. The mean impacts for all treatment variants are significant. The pooled treatment has a significant mean impact of 3.3 ppts. Given a baseline exam participation rate of 97 percent, the mean impact maximizes the rate.

Third, with respect to student exam scores, mean impacts of the treatment variants do not exhibit a monotonic pattern of change over the three years. Only treatment variant _HT+_ has consistent positive mean impacts of .09, .14, and .05 sds in the first, second, and third years, but they are all insignificant. Mean impacts across the years for the other two treatment variants are also all insignificant. The pooled treatment has mean impacts of .02, .08, and –.02 sds in the first, second, and third years, but they are all insignificant.

# _Potential strategic manipulation of enrollment data_

The significant positive mean impacts on school enrollment in the third year may be due to program schools inflating enrollment. While schools do not have direct control over ASC

> we find significant mean impacts mainly in the third year. Results from pairwise and joint tests suggest that baseline means do not differ between treated and untreated schools in the attrited sample.

16
**Figure 4. Contribution of oil supply and demand shock**

# **A. Oil price decomposition**

# **B. Share of oil price decline driven by supply shocks**

<!-- Start of picture text --> Cumulative percent change from Jan. 1992 Percent<br>200 60<br>Demand<br>150 50<br>Supply<br>100 Oil price 40<br>50 30<br>20<br>0<br>10<br>-50<br>0<br>-100 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Jun 08 - Dec 08 May 14 - Feb 15 May 15 - Feb 16<br><!-- End of picture text --> Source: World Bank.

A. B. Based on the decomposition of oil price changes from a structural vector autoregressive (SVAR) model including global industrial production, global oil production, and oil and metals prices. The identification scheme is comparable to that suggested in Caldara, Cavallo, and Iacoviello (2016), putting restrictions on short-term oil supply and demand elasticities based on a survey of the literature.

A. Last observation is February 2018.

18
_Entrep_ , suggesting that access to finance itself directly and powerfully affects _Female Entrep_ , but its effect does not hinge on the FDI level and its distribution across industries. In some sense then, access to finance by women is a robust facilitator of female entrepreneurship.

# **Women’s empowerment and FDI effects on female entrepreneurship**

Women’s empowerment is supposed to facilitate women’s entrepreneurship. Is this true in our comprehensive data? How do FDI and female empowerment interact with each other in influencing female entrepreneurship? To answer these questions, we include two aspects of female empowerment: female labor participation rate, and female education (as proxied by the completion rate of primary schools among adult females).<sup>10</sup> It is useful to note that these two measures capture distinct aspects of female empowerment: the former captures general economic participation, and the latter captures improvement in human capital. Table 11 reports the results.<sup>11</sup> When female labor participation is higher, women tend to face lower discrimination and have better access to scarce resources. Not surprisingly, this empowerment measure itself is associated with higher female entrepreneurship. Increasing it by one standard deviation (i.e., 16) is associated with higher _Female Entrep_ by 3.2 percentage points (when evaluated at the mean of the interacting FDI variables), a big effect.

Interestingly, when a country’s female labor participation rate is higher, FDI’s within-industry crowding out effect is more limited, perhaps because women face less serious obstacles in access to scarce resources (such as power and finance), and the market stealing effect is thus not strongly biased against women entrepreneurs. Based on the estimates in column (1), the coefficient of c is -0.096 (0.064) at the mean (one SD above the mean) of female labor participation rate. Thus, higher female labor force participation FF thus facilitates female entrepreneurship, and could change the intra-industry FDI effects from crowding out to crowding in female entrepreneurship **_._** In contrast, better female education is associated with higher female

> 10 Both variables come from the World Development Indicators.

> 11 To the extent that the rule of law may also level the playing field, especially for women, we have also examined whether the FDI effects depend on a country’s level of rule of law. We have not found it to matter much.

16
10 shared address appears only once in the EconLit record, and we count the article only once for that institution. Where an author has multiple affiliations, these appear on the same line in the EconLit record, we retained only the first-listed address—the first-listed institution gets all the credit. Adopting these rules, we inevitably end up with more records than publications: we have over 37,000 ―publications‖ just for the sub-period 1981-2010.

We then aggregated these ―publications‖ by address, and then by institution. Sometimes, an author‘s address was the institution itself, and the aggregation involved simply checking for variations in spelling or format and deciding on a common one. So, for example, the US Department of Agriculture sometimes appeared as ―USDA‖ and sometimes as ―US Department of Agriculture‖; we renamed the former ―US Department of Agriculture‖ and the two sets of records were aggregated. Other examples include the World Health Organization (which appeared also as WHO) and the International Food Policy Research Institute (which also appeared as IFRPI). Sometimes, the author address was not an institution but rather a unit within an institution. For example, authors affiliated to the University of York‘s Centre for Health Economics (CHE) often listed their address as CHE not the University of York. Other authors at York listed their address as the Department of Economics and Related Studies. Often these and other addresses appeared in multiple forms, with, for example, abbreviations. In such cases, we aggregated records up to the institution level—the University of York in the example above. The same rule was applied to records relating to the colleges of the universities of Cambridge and Oxford, but was not applied to constituent colleges of state universities in the US or the universities of London and Wales.<sup>15</sup> So, Berkeley stayed as University of California Berkeley, and the London School of Economics and the London School of Hygiene and Tropical Medicine (both part of the University of London) stayed as distinct institutions.

We identified 13,500 distinct author addresses in our dataset. We quickly realized that the cleaning and aggregation process would require a lot of manual input by someone familiar with the names and common abbreviations thereof of academic institutions and think tanks. To turn this into a manageable exercise, we required that an address had to occur at least five times for it to be retained for cleaning and aggregation. So, for example, the inappropriately Americanized

> 15 Some authors listed their affiliation simply as the University of London; in such cases, there was obviously no choice but to leave the affiliation at the university rather than college level.