rafmacalaba/datause-encoder-data huggingface dataset:
Manpower planners can assist managers of training programs and trainees in choosing cost-effective training strategies. In most occupations for which training programs are designed there are many alternative paths to skills acquisition. These laths include combinations of general and technical schooling, in-plant and outside-plant vocational training in full-time and part-time training modes, and onthe-job learning (Godfrey, 1991:59) To identify these training strategies, planners can use reverse tracer studies. This planning technique identifies an occupation for which a training program has been designed and surveys a sample of those employed in the occupation (Dougherty, 1988). Respondents are asked about prior schooling, employment, and training. The information is used to construct an occupational map illustrating the diverse paths of entry into an occupation and the sources of training. Where multiple entry paths are found, which is the case in most occupations, planners can then pursue the measurement of labor market outcomes and costs associated with the alternative paths.

The information produced by reverse tracer studies provides signals for guiding choices of cost-effective training strategies. Where there are many cost-effective training alternatives available in a market economy to meet skill needs of particular occupations, this is a signal that markets are working well and public interventions are unnecessarv. In making this judgement, the measurement of training costs is important. Manpower planners need to spend considerable time in developing cost studies of alternative training programs and strategies. These cost estimates are important to managers of training programs and trainees in developing implicit or explicit labor market signals such as rate of return estimates and cost-effectiveness measures. Planners can improve the functioning of labor markets and skills development by producing cost estimates of alternative training programs and disseminating this information as a public good.

The determination of education and training program costs is important as a benchmark for efforts by managers to improve the cost-effectiveness of these programs. By comparing the cost of education and training programs with similar objectives, managers can identify programs where attention needs to be given to cost management. Cost estimates as signals of need for managers to improve the intemal efficiency of education and training programs require the measurement of direct and indirect 13
The estimates of the model coefficients are found to be largely coherent: (a) per capita expenditure decreases with household size with a declining marginal effect, (b) the `returns to education’ are all positive with higher returns for higher education levels (i.e. tertiary > secondary > primary education coefficient), (c) unemployment enters negatively, while waged-, self-employment and being an employer all enter the regression positively, (d) public sector employment too enters positively, while employment in the BTP sector (construction) is associated with lower standard of living, which is consistent with the BTP being a low-wage sector, (e) employment in the financial sector is clearly beneficial, but only in 2007, it was not yet significant in 2001, (f) size of the house as measured by the number of rooms per capita is strongly positively associated with total household expenditure, although the marginal effect declines for larger houses as expected, (g) similarly, households equipped with electricity, sewage, in-house clean drinking water (as well as the added durable assets and housing variables) are found to have higher total expenditure on average, (h) the significance of the interactions with the region dummy variables shows that the significance of the above mentioned variables is stronger for some areas than for others.

**Table 2 – Urban Model**

||**Wit**|**hout Addit**|**ional Assets**||**W**|**ith Additio**|**nal Assets**||
|---|---|---|---|---|---|---|---|---|
|**Variable**|**2001**||**2007**||**2001**||**2007**||
|Domain U2|-0.233|***|-0.132||-0.178|***|-0.122||
|Domain U3|-0.041||-0.160|**|-0.031||-0.070||
|Domain U4|-0.103|**|-0.191|**|-0.043||-0.145|*|
|Domain U5|0.000||-0.108||0.127|***|-0.026||
|Hhld size|-0.090|***|-0.113|***|-0.140|***|-0.169|***|
|Hhld size^2|0.002|***|0.005|***|0.005|***|0.008|***|
|Log age (head)|0.082|***|0.108|***|0.070|***|0.082|***|
|Married (head)|0.096|***|0.140|***|0.070|***|0.114|***|
|Primary (head)|0.101|***|0.071|***|0.069|***|0.037|*|
|Secondary (head)|0.231|***|0.187|***|0.149|***|0.114|***|
|Tertiary (head)|0.489|***|0.439|***|0.382|***|0.352|***|
|Employed (head)|-0.055||-0.191|*|-0.043||-0.193|**|
|Unemployed (head)|-0.196|***|-0.308|***|-0.144|***|-0.300|***|
|Selfemployed (head)|0.098|***|0.191|***|0.159|***|0.265|***|
|Employer (head)|0.296|***|0.403|***|0.226|***|0.353|***|
|Employer (count)|0.385|**|0.749|***|0.394|***|0.721|***|
|Public (count)|0.315|***|0.300|***|0.249|***|0.240|***|
|BTP (head)|-0.097|***|-0.127|***|-0.077|***|-0.091|***|
|Finance (head)|||0.135|*|||0.160|**|
|Finance (count)|||0.580|***|||0.447|**|
|Waged (count)|0.222|***|0.253|***|0.264|***|0.339|***|
|Primary 1 (count)|0.122|***|0.145|***|0.039||0.106|***|
|Primary 2 (count)|0.420|***|0.369|***|0.266|***|0.234|***|
|Secondary (count)|0.639|***|0.485|***|0.412|***|0.355|***|
|Tertiary (count)|0.684|***|0.795|***|0.470|***|0.636|***|

14
A year later, therapy participants also described applying skills of self-regulation in their lives. To avoid fights, they used new tactics: removing themselves from emotionally-charged situations, allowing space to process their feelings, and ignoring negative automatic thoughts in the favor of more controlled thinking. Related were improved social and communication skills. Interviewees described how such skills allowed them to engage with community members or in disputes and express themselves without anger or violence.

Not only did the community regard them differently, many said, but troubled young men began coming to them for advice and lessons learned from the therapy once they saw the sudden and sustained change–another important source of reinforcement, and perhaps one reason we do not see a change in peer quality in the data.

# **7 Can we believe our self-reported data?**

Self-reported data raise several worries, the most serious being measurement error correlated with treatment. For instance, men who receive an anti-violence intervention might be more likely to tell us they are non-violent, overestimating the estimated treatment effect of therapy.

This kind of bias is hard to square with the patterns of effects we observed. Therapy followed by cash would have to induce systematic errors where therapy or cash alone did not. Nonetheless, this is possible. Thus, concerned that our survey measure, _y_<sup>_s_</sup> , may be biased, we set out to intensively validate some measures, _y_<sup>_v_</sup> . If _y_<sup>_v_</sup> is closer to the true behavior, _y_<sup>_∗_</sup> , this allows us to estimate the degree and direction of bias. We summarize the approach, empirical strategy, and results here, with details in Appendix F and Blattman et al. (2015).

## **7.1 Approach to validation**

Of more than 4,000 endline surveys, we randomly selected 7.3% and validated answers to six survey-based measures with two-week recall periods. We chose four potentially sensitive behaviors—marijuana use, thievery, gambling, and homelessness. We also chose two everyday expenditures that we did not consider sensitive but could be subject to recall bias or other error—paying to watch television in a video club, and paying to charge a mobile phone.

We used intense qualitative work—in-depth participant observation, open-ended questioning, and efforts to build relationships and trust—to try to elicit more truthful answers. Over several days of trust-building and conversation, plus direct observation, we tried to elicit a direct admission or discussion of the behavior.

We selected and trained eight of the study’s most talented qualitative research staff as validators, all Liberians. In the ten days following the survey, a validator visited the respondent over four days, spending several hours each day in conversation and observation.

32
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whole set is only _ρ_ = 0.17. In a second exercise, we calculate the correlation of the blood test result against an index of equally-weighted symptoms that are commonly associated with blood poisoning (convulsions, diarrhea, dizziness, eye irritation, headaches, shortness of breath and vomiting). The composite correlation is even weaker: _ρ_ = 0.05. We conclude that our evidence does not support the use of self-reported symptoms as indicators of actual pesticide poisoning. Given the high incidence of poisoning in the sample, however, our results strongly support regular medical testing for farmers who are exposed to highly- or moderately-toxic organophosphate and/or carbamate pesticides.

# **5.  Determinants of Cholinesterase Inhibition**

Using the blood cholinesterase test results, we conduct a regression analysis to identify the major determinants of pesticide poisoning. According to the medical literature, the types and severity of cholinesterase inhibition symptoms depend on the toxicity of the pesticide, the amount of pesticide involved in the exposure, the route of exposure, and the duration of exposure (Extension Toxicology Network, 2004). The health effects of pesticide use can often be reduced significantly by appropriate use of protective items such as gloves, hats, jackets, eye glasses or masks (Cropper, 1994). A priori, we would expect farmers’ self-protective behavior to depend on their perception of risk, along with the quality of their information about safe handling of pesticides. Taking these factors into account, we specify and estimate the following regression model with dummy variables to control for provincial differences.

# POISONING = f (PESTAMT, PWHOIab, PWHOII, PWHOIII, PWHOU, PTRAIN, NPROTECT, IPMD, Provincial Dummies*)

where POISONING = 1 if acute/chronic poisoning indicated by cholinesterase test, 0 otherwise; PESTAMT = total amount of pesticides applied (kilograms); PWHOIab = % of pesticides classified as highly hazardous (WHO category Ia or Ib); PWHOII = % of pesticides classified as moderately hazardous (WHO category II); PWHOIII = % of pesticides classified as slightly hazardous (WHO category III); PWHOU = % of pesticides with no significant hazard in normal use (WHO category U); PTRAIN = 1 if trained in safe handling of pesticides, 0 otherwise; NPROTECT = protective items used (1= if more than 2, 0 otherwise); IPMD = 1 if Integrated Pest Management techniques employed, 0 otherwise;

* Dummy for Tra Vinh province dropped to permit estimation.

9
group, when outages frequency exceeds this relatively low threshold, small firms are virtually unable to operate and the sectors are populated only by large firms owning generators.

Finally, we explore the relevance of the results from corollary 1 above, by estimating equation (8) on different firm size subsamples (respectively small, medium and large firms). We report the results in Table 10. First, note that the number of power outage always significantly increases the probability that small, medium or large firms own complementary capital, although the effect is larger for medium firms. Second, the quadratic term is not significant when considering small firms only, highlighting the fact that the non-linear effect of outages is mostly relevant for medium and, to a lesser extent, large firms. Overall, this confirms our theoretical insight that variations in power outages affect mostly medium-size firms.

**Table 10:** **<u>Complementary Capital Decision and Firm Size</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Probit|Generator|Generator|Generator|Generator|Generator|Generator|
||Small Fir|ms Sample|Medium Fir|ms Sample|Large Fir|ms Sample|
|Number of Power Outage (/100)|0.243|0.270|0.387|0.389|0.236|0.207|
||(1.90)*|(1.84)*|(4.26)***|(3.78)***|(3.53)***|(3.15)***|
|Number of Power Outage (/100) Square|-0.037|-0.046|-0.068|-0.067|-0.024|-0.023|
||(1.02)|(1.14)|(2.79)***|(2.62)***|(3.42)***|(3.49)***|
|Access to Credit is Major/Severe Constraint (dummy)|-0.078|-0.119|-0.099|-0.121|-0.127|-0.159|
||(1.22)|(1.77)*|(2.08)**|(2.12)**|(2.13)**|(2.34)**|
|Access to Credit is Major/Severe Constraint (dummy) *|-0.011|-0.003|-0.035|-0.021|-0.024|-0.009|
|Number of Power Outage (/100)|(0.16)|(0.04)|(0.76)|(0.46)|(0.43)|(0.16)|
|Age||0.005||0.004||0.003|
|||(2.61)***||(2.30)**||(2.68)***|
|Capital City dummy||-0.022||0.188||0.147|
|||(0.30)||(2.39)**||(1.51)|
|Export dummy||0.267||0.280||0.258|
|||(2.80)***||(4.54)***||(4.30)***|
|Foreign dummy||0.426||0.142||0.243|
|||(4.26)***||(2.06)**||(3.69)***|
|Constant|-0.453|-0.365|-0.473|-0.939|-1.286|-1.846|
||(1.15)|(0.64)|(1.13)|(2.99)***|(2.17)**|(3.15)***|
|Country dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|6 056|4 375|7 318|5 178|5 317|4 079|
|Threshold value δ= -α1/2α2|3.44|2.95|2.81|2.83|4.70|4.61|

Absolute value of z statistics in parentheses, standard errors are clustered at the country-industry level. * significant at 10%; ** significant at 5%; *** significant at 1%
Research Working Paper (PRWP) Series or working paper series hosted at other institutions (107), and books (13).<sup>4</sup> We then moved to SSCI adding 2,362 previously undiscovered publications (all journal articles) with the World Bank listed as the author’s address. Next we searched EconLit, finding 6,080 further publications authored by World Bank staff. These included 4,658 book chapters, 1,344 journal articles, and 78 working papers not in the PRWP series. We next added items in the World Bank’s e-Library not already in our database: this resulted in us adding 3,545 papers in the PRWP series, 506 edited books, and 1,702 books published by the World Bank. Finally, we added from the US Library of Congress catalog 238 books co-published by the Bank and university presses not included in the Bank’s e-Library.

Our final database contains 20,051 publications, comprising 9,077 journal articles, 4,672 book chapters, 3,730 working papers, 1,953 books, 506 edited volumes, and 113 conference proceedings. There is some double-counting, since a share—we would guess half—of the working papers re-appear as journal articles. Even so, that leaves some 18,000 distinct products.

A basic message to emerge from our analysis, therefore, is that the vast majority of the Bank’s publications are not books but rather articles, book chapters and working papers. Nearly half (45%) of the publications by Bank authors are actually published by other publishers in scholarly journals. Yet this sizeable output of the Bank’s work is not catalogued in its online Documents and Reports database.<sup>5</sup> Figure 1 tracks the composition of the Bank’s publications since the early 1970s. (To see the trends more clearly, we have presented the data as 7-year moving averages.) We see that the number of books per year has remained fairly constant since the mid-1990s; we see some limited growth in edited volumes and book chapters.<sup>6</sup> However, the big growth has come from papers— journal articles and working papers. Some of this growth is artificial, stemming from expanding coverage of the bibliographic datasets; for example, EconLit only started including book chapters in 1987. Nonetheless it is clear that there has been substantial growth since 1990.

How does the Bank’s publication count compare with other institutions? In Table 1 we compare the Bank’s journal article counts with those of 14 comparator universities—chosen to represent the top universities for economics in the US and UK—and seven comparator

> 4  Some of the SCOPUS publications were initially classified as “generic”, and had to be manually reclassified.

> 5  The database is available at <u>http://www-wds.worldbank.org/.</u>

> 6  Not all book chapters in our database are in Bank-edited volumes. The North-Holland Handbook chapters are an example.

7
<!-- Start of picture text --> Figure 10: Investment shares ( % of GDP)<br>20%<br>15%<br>10%<br>5%<br>0%<br>2000 2002 2004 2006 2008<br>Business Intangible Capital Investment Business Tangible Capital Investment<br>Gross Fixed Capital Formation (%GDP)<br><!-- End of picture text --> Figure 11 compares tangible and intangible investment by industry. There is a hint here that the ratio of intangible to tangible investment is lower in more ̳low tech‘ industries, or that in other words, knowledge-related capital matters more in industries which are technologically more sophisticated on average. But in general, Brazilian manufacturing firms still spend more on tangible capital. In contrast, the service sector is already spending more on intangible assets than on tangible assets (and this pattern would be even stronger if we could account for omission of labor training expenditure). Given that services account for a rising share of the economy, this finding may have important implications for policies for encouraging innovation and growth.

<!-- Start of picture text --> Figure 11: Estimated Investment (2000-08 average, % of GDP)<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Extractive Low tech Medium/High tech ICT Services Services (except ICT)<br>Manufacturing Manufacturing<br>Business Intangible Capital Investment Business Tangible Capital Investment<br><!-- End of picture text --> 27
# **Table 7: Estimation of trade gravity regression: IV Results**

IV: Crude oil price*VAT rate in the next potential destination

||Control 1|Control 2|Control 3|
|---|---|---|---|
|**Ln (value of trade)**|(1)|(2)|(3)|
|Ln (Freight rate)|-2.790**|-3.656***|-2.836**|
||(1.277)|(0.880)|(1.269)|
|**Ln (Volume of trade)**||||
|Ln (Freight rate)|-3.410***|-4.275***|-3.459***|
||(1.229)|(0.877)|(1.221)|
|Kleibergen-Paap F statistic|14.54|28.67|14.59|
|Origin Fixed Effects|Yes|Yes|Yes|
|Destination Fixed Effects|Yes|Yes|Yes|
|No. Absorbed FEs|1080|1003|1080|
|No. OD pairs|23288|13678|23288|

Note: Dependent variables are in bold. Each cell represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price). Control 2: Control 1 plus ln (diesel price/litre along OD route). Control 3: Control 1 plus ln (average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

**Table 8: Estimation for Extensive margin of trade**

||Ln (Freight Rate)<br>OLS|Ln (numb<br>OLS|er of trips)<br>IV|
|---|---|---|---|
||(1)|(2)|(3)|
|Av. VAT rate at next probable destinations|10.485***<br>(1.672)|||
|Ln (Freight rate)||-0.175***|-1.232***|
|||(0.045)|(0.409)|
|Kleibergen-Paap F statistic|||39.31|
|No. OD pairs|24,445|24,445|24,445|

Note: Each column represents a coefficient estimate from separate regressions. The column heading corresponds to the dependent variable. All regression includes the following controls: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price), ln (night lights at origin), ln(night lights at destination), ln(night lights at the route level). Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

29
**Table 11. Data-generating processes and inequality measures under scenarios in which pseudoindividuals are allocated to each type according to a normal distribution**

||**Ine**|**quality m**|**easures**||**R‐s**|**quared by**|**observed**|**circumstance**<br>**Father’s**|**Mother’s**|
|---|---|---|---|---|---|---|---|---|---|
|**Error**<br>**Distribution**|**Gini**<br>**(1)**|**MLD**<br>**(2)**|**IO Ratio**<br>**(3)**|**All**<br>**(4)**|**Female**<br>**(5)**|**Urban**<br>**(6)**|**Region**<br>**(7)**|**Education**<br>**(8)**|**education**<br>**(9)**|
|||||_Pan_|_el A. Baseli_|_ne Scenar_|_io_|||
|_N(0,0.1)_|0.076|0.003|0.974|0.975|0.302|0.074|0.522|0.026|0.094|
|_N(0,0.3)_|0.095|0.005|0.602|0.614|0.189|0.047|0.329|0.016|0.059|
|_N(0,0.7)_|0.111|0.006|0.431|0.444|0.137|0.034|0.238|0.013|0.042|
|_N(0,1.0)_|0.140|0.010|0.270|0.283|0.088|0.021|0.152|0.008|0.026|
|_N(0,1.2)_|0.193|0.020|0.136|0.148|0.044|0.011|0.081|0.004|0.014|
|_N(0,1.5)_|0.249|0.035|0.080|0.091|0.028|0.007|0.049|0.003|0.009|
|||||_Panel B. I_|_nequality E_|_nhanced S_|_cenario_|||
|_N(0,0.1)_|0.198|0.020|0.786|0.665|0.155|0.083|0.334|0.031|0.105|
|_N(0,0.3)_|0.206|0.022|0.730|0.623|0.145|0.078|0.313|0.029|0.098|
|_N(0,0.7)_|0.212|0.023|0.678|0.584|0.136|0.072|0.294|0.027|0.093|
|_N(0,1.0)_|0.225|0.027|0.593|0.518|0.121|0.064|0.260|0.024|0.082|
|_N(0,1.2)_|0.255|0.036|0.446|0.405|0.094|0.050|0.203|0.019|0.065|
|_N(0,1.5)_|0.290|0.048|0.326|0.309|0.071|0.037|0.156|0.015|0.049|
|**Urban**||||||||||
|**Indicator's**||||||||||
|**coefficient**||||_Panel C_|_. Increase‐i_|_n‐IOO Sce_|_nario_|||
|_0_|0.141|0.010|0.256|0.269|0.089|0.000|0.155|0.006|0.026|
|_10_|0.146|0.011|0.378|0.391|0.075|0.167|0.135|0.008|0.027|
|_15_|0.155|0.012|0.479|0.493|0.064|0.305|0.117|0.010|0.026|
|_25_|0.181|0.017|0.651|0.668|0.041|0.545|0.081|0.009|0.022|
|_35_|0.211|0.023|0.761|0.778|0.027|0.696|0.057|0.009|0.018|
|_50_|0.256|0.035|0.851|0.870|0.016|0.823|0.037|0.007|0.014|

Notes: Data generating process is presented in equation (3), and modified as described in robustness checks section.

29