rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3.6.1 : Additional EU Data Included in AD-EUN-v1.0.xls**

|**Column**|<br>**Variable Name**|**Description**|
|---|---|---|
|**In AD-E**|**UN-Master :**||
|**U.**|**INIT_GP**|The government publication containing the initiation notice|
|**V.**|**P_AD_DUTY**|The preliminary antidumping duty imposed. If there were several duties<br>imposed, this column contains the maximum dumping duty|
|**W.**|**F_AD_DUTY**|The final antidumping duty imposed. If there were several duties<br>imposed this column contains the maximum dumping duty|
|**X.**|**P_AD_MARGIN**|,<br>The preliminary dumping margin assessed. If there were several<br>dumping margins assessed, this column contains the maximum dumping<br>margin found|
|**Y.**|**F_AD_MARGIN**|The final dumping margin assessed. If there were several dumping<br>margins assessed, this column contains the maximum dumping margin<br>found|
|**Z.**|**P_GP**|The government publication containing the preliminary outcome of the<br>investigation|
|**AA.**|**F_GP**|The government publication containing the final outcome of the<br>investigation|
|**AB.**|**NOTES**|Comments|
|**In AD-E**|**UN-Products :**||
|**D.**|**CN_ P_ex**|This column indicates whether the HS code (referred to as the CN code<br>in the European Union) of the good under investigation has an ‘ex’<br>prefix at the preliminary stage. The ‘ex’ prefix indicates that the<br>products being investigated are only a subset of the products in the<br>reported CN code.|
|**E.**<br>**F.**|**P_HS_CODE**<br>**SIG_DIGITS_P_HSCODE**|The HS code of the investigated good at the preliminary stage<br>Number of digits of the HS product code reported at the preliminary<br>stage.|
|**G.**|**CN_F_ex**|<br>This column indicates whether the HS code (referred to as the CN code<br>in the European Union) of the good under investigation has an ‘ex’<br>prefix at the final stage. The ‘ex’ prefix indicates that the products being<br>iid l b f h d i h d CN d|
|||nvestgate are ony a suset o te proucts n te reporte coe.|
|**H.**|**F_HS_CODE**|The HS code of the investigated good at the final stage|
|**I.**|**SIG_DIGITS_F_HSCODE**|Number of digits of the HS product code reported at the final stage|
|**J.**|**NOTES**|Comments|
|**In AD-E**|**UN-Domestic-Firms :**||
|**C.**|**COUNTRY**|The European country in which the plaintiff domestic firm is located|
|**In AD-E**|**UN-Foreign-Firms :**||
|**D.**|**PADMEASUREFIRM**|The firm-specific preliminary antidumping measure imposed|
|**E.**|**___**<br>**P_AD_MARGIN**|<br>The firm-specific preliminary antidumping margin imposed|
|**F.**|**F_AD_MARGIN**|The firm-specific final antidumping margin imposed|
|**G.**|**P_AD_DUTY**|The firm-specific preliminary antidumping duty imposed|
|**H.**|**F_AD_DUTY**|The firm-specific final antidumping duty imposed|
|**I.**|**NOTES**|Comments|

In terms of missing or incomplete data, the European Union dataset lacks information on reviews.

24
effects of human capital formation (see Cruz et al. 2017). In our global setting, the household survey data in the updated GIDD spans 85% of the global population.<sup>9</sup> In the analysis, GLOBE_DYN provides economy-wide implications of macroeconomic projections under the baseline and the different AMR scenarios. For each scenario, the evolution of relative prices and factor rewards, including wages of skilled and unskilled workers in agriculture and non-agriculture sectors are passed on as inputs to the GIDD. Over time, GIDD generates reweighted household surveys that account for the changing household composition due to demographic trends (e.g. aging, education or skill attainment, etc.) as well as changes in the labor supply from population growth and AMR-related mortality. Drawing on this information, the behavioral model at the household level regenerates new patterns of household employment, income, and consumption in the household sample surveys across regions and over time to reflect the effects of the baseline and AMR scenarios. Using these results, GIDD computes the poverty and inequality indicators for each scenario.

# **III. The Baseline - A World Without AMR**

The baseline characterizes “a world without AMR,” which follows the current long-term economic growth projections of the World Bank. It is based on data on GDP growth, investment growth, factor supply growth, the evolution of the internal balance (fiscal budget deficit or surplus), and the evolution of the external (current account) balance. To generate general equilibrium solutions that are consistent with these projections, the baseline makes the following assumptions regarding macro closures and conditions for market clearing:

- Regional TFP is made endogenous to track generate exogenous regional GDP levels. Changes in TFP directly affect value added (or GDP at factor cost).

- Adjustments in regional savings rates make sure that, in each region, total savings are sufficient to finance investment, defined as exogenous shares of GDP to match the projections of the World Bank’s _Global Economic Prospects_ (GEP). With regard to investment, the analysis uses GEP investment data and forecasts for the period 2008-2017. A steady-state GDP investment share of 20% is assumed in all regions in 2030 and beyond. A linear adjustment is imposed between 2017 and 2030, linking projected 2017 values to the steady state.

> 9 In single-country applications of microsimulation such as the Philippines (Bourguignon and Savard 2008) or South Africa (Go et al. 2010), the availability of richer data sets has made it possible to use an income-generation model with an elaborate occupational-choice behavioral module at the household level.  That approach permits wages and employment probability of individuals to become the functions of diverse household characteristics, such as age, gender, the level of schooling, years of work experience, sector of employment, location (urban or rural, family size, and additional household data.

7
Table 2: Program Impacts on Grades 3 and 5 Test Scores

|Panel A: Beginnin|French<br>g of Grade|Math<br> (First Foll|Oral<br>ow-Up)|Index|PPVT|
|---|---|---|---|---|---|
|Overall|0.021<sup>_∗∗_</sup>_†_|0.019<sup>_∗∗_</sup>_†_|0.019<sup>_∗_</sup>_†_|0.080<sup>_∗_</sup>_†_||
||(0.010)|(0.010)|(0.010)|(0.044)||
|Observations|5368|5361|2732|2679||
|Control Mean (SD)|0.51 (0.23)|0.49 (0.23)|0.50 (0.27)|||
|Grade 3|0.029<sup>_∗∗_</sup>_†_|0.027<sup>_∗∗_</sup>_†_|0.029<sup>_∗∗_</sup>_†_|0.126<sup>_∗∗_</sup>_†_||
||(0.014)|(0.012)|(0.014)|(0.060)||
|Observations|2720|2718|1385|1350||
|Control Mean (SD)|0.53 (0.25)|0.54 (0.24)|0.35 (0.22)|||
|Grade 5|0.011|0.010|0.008|0.027||
||(0.011)|(0.012)|(0.013)|(0.053)||
|Observations|2648|2643|1347|1329||
|Control Mean (SD)|0.48 (0.20)|0.44 (0.20)|0.64 (0.24)|||
|Panel B: End of G<br>Overall|rade (Seco<br>0.020<sup>_∗_</sup>|nd Follow-<br>0.005|Up)<br>0.026<sup>_∗∗_</sup>_†_|0.094<sup>_∗_</sup>_†_|0.057|
||(0.012)|(0.011)|(0.012)|(0.054)|(0.082)|
|Observations|3338|3327|1686|1620|1122|
|Control Mean (SD)|0.63 (0.22)|0.62 (0.22)|0.58 (0.26)|||
|Grade 3|0.035<sup>_∗∗_</sup>_†_|0.017|0.039<sup>_∗∗_</sup>_†_|0.160<sup>_∗∗_</sup>_†_|0.153|
||(0.016)|(0.015)|(0.018)|(0.077)|(0.096)|
|Observations|1732|1721|853|826|566|
|Control Mean (SD)|0.66 (0.23)|0.68 (0.23)|0.45 (0.23)|||
|Grade 5|0.003|-0.008|0.007|0.013|-0.060|
||(0.012)|(0.013)|(0.014)|(0.061)|(0.097)|
|Observations|1606|1606|833|794|556|
|Control Mean (SD)|0.59 (0.20)|0.57 (0.20)|0.72 (0.21)|||

Notes: Standard errors are in parentheses and are adjusted for clustering. _∗ p <_ 0 _._ 10 , _∗∗ p <_ 0 _._ 05 ,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 correspond to p-values from the usual single-hypothesis tests. _†_ corresponds signicance at the 10% level of Romano Wolf (2005) p-values from joint tests of French, mathematics, and oral (3 tests each, by row) or to the index alone. Conditioning variables: Grade, gender, household size, number of children, education of head, distance to school, wealth index, interview language, baseline scores, missing dummies.

13
- Valletti, T.M., S. Hoernig and P.P. Barros (2001), "Universal Service and Entry: The Role of Uniform Pricing and Coverage Constraints," _Journal of Regulatory Economics_ (forthcoming).

- Wallsten, S. (2001), "An Econometric Analysis of Telecom Competition, Privatization and Regulation in Africa and Latin America," _Journal of Industrial Economics_ 49(1): 1-19.

39
**Table 9:  Analysis of bundled contract attributes at company level**

||DV is log average<br>wage rate paid on<br>oDesk|DV is cumulative<br>percentage<br>differential between<br>contracts and median<br>proposals|DV is average "good<br>performance" ratings<br>over contracts from<br>feedback|DV is average "good<br>performance" ratings<br>over contracts from<br>private success survey|DV is number of<br>workers hired divided<br>by total number of<br>contracts|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||Estimates inclu|Each observation is a u<br>de fixed effects for com|nique company contact<br>pany's first year x moda|located outside of India<br>l job type x country of|<br>company contact|
|Share of contracts that are|-0.135***|-0.075***|-0.070***|-0.071***|0.021***|
|formed with workers in India|(0.008)|(0.005)|(0.007)|(0.007)|(0.002)|
|Share x ethnic Indian company|0.016|0.021|0.000|0.014|-0.002|
|contact|(0.034)|(0.015)|(0.031)|(0.030)|(0.009)|
|Ethnic Indian company contact|-0.041*|-0.025***|-0.001|-0.002|0.007|
||(0.023)|(0.010)|(0.016)|(0.015)|(0.005)|
|Observations|35,863|35,862|30,097|29,889|35,863|
|Mean of dependent variable|2.088|0.026|0.510|0.637|0.935|

Notes: Company contact-level regressions estimate wage and performance effects from ethnicity-based contracts using variation among company contacts located outside of India. Regressions are unweighted, include fixed effects for first year x modal job type x country of company contact, and report robust standard errors. Performance observation counts are lower due to ongoing jobs (99% of cases) or missing values. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
# **3 Methodology**

## **3.1 Research Design**

Our impact evaluation focuses on 36 urban public works sites in 17 communes of the PEJDC intervention area in 9 regions of Burkina Faso (Boucle du Mouhoun, Centre, Centre-East, CentreNorth, Centre-West, Centre-South, Hauts Bassins, North, Plateau Central). The 36 sites were selected based on their potential to support a childcare center. The number of participants at each site ranged from 100 to 500 and the proportion of women ranged from to 58% to 96%.

We used a computer program to randomly assign the 36 urban public works sites in the research sample to a treatment group (18 sites) with implementation of community-based childcare centers and a control group (18 sites) with no childcare centers. To increase the similarity of the 2 groups, the randomization was stratified across 10 blocks based on the geographical location and number of brigadiers assigned to each site. The sites were defined by _communes_ (municipalities) in smaller urban areas and by _arrondissements_ (neighborhoods) in the two larger cities of Ouagadougou and Bobo-Dioulasso. Figure 1 illustrates the spatial distribution of our study sites.

## **3.2 Sampling**

Our sample comes from the register of public works participants in the 36 study sites. We began with a screening survey to restrict our sampling frame to women who were primary caregivers for a child aged 0 to 6 (because the centers were open to children below the age of 7). Among participants meeting these eligibility criteria, we randomly selected 2,160 individuals. In each of the 36 sites, the size of the sample was proportional to the number of eligible participants in that site. The study sample therefore included 2,160 households, with data collected on the 2,160 female public works participants, their spouses or partners if co-residing, and any children aged 0-6 in their care, for a total of up to 4,320 individual interviews and a minimum of 2,160 child assessments (Figure 2 presents our sampling strategy).<sup>7</sup>

> 7With this sample of 36 sites (18 per treatment group), an average of 60 participants per site, power of 0.8, significance level of 0.05, and an estimated intracluster correlation of 0.1, the study was designed to detect a minimum effect of 0.2 standard deviations.

10
10 The link between the transformation programs and the growth in exports of industrial products is apparent in data presented in Tables 1 and 2: the share of industrial products in exports to the EC (see Table 1) increased during the first year of the program (Poland-1990; the FCSK-1991; Bulgaria-1992); and the average growth rate of industrial exports in the 198891 period was higher than that of non-industrial exports for the txoika countries while it was lower for the two Balkan countries (Bulgaria and Romania).

Table 1: The Rdative Imporbace of Iudustrial Products in CEE-5 Exports **to the** EC, **19892**

|||Expor<br>(valu|ts of Indu<br>e in US$|strial Pro<br>million)|ducts<br>|Sbare i|n Total E<br>(n per|xports to<br>cent)|the EC||
|---|---|---|---|---|---|---|---|---|---|---|
||1988|<br>1989|1990|1991|1992|1988|1989|1990|1991|199Z|
|Bulgaria|418|430|537|677|899|73|72|72|73|79|
|FCSK|2367|2515|3053|4643|6687|91|89|90|92|93|
|Hungary|1777|<br>1961|2795-|3331|4034|69|68|74|73|77|
|Poland|3092|3230|5089|6247|7793|78|75|77|81|_85_|
|Romania|2522|2649|1908|1706|1697|95|95|97|94|93|

Since Greece, as of July 1993, has not reported trade data for 1992, its ammal imports estimated on the basis of first the quarters.

# Source: the United Nations CONTRRADE data base

Eaier market access for industrial products than for agncltural products has clearly faciltated this increase in exports to te EC. As can be seen from data compiled in Table 2, indlstrial exports fiLced significantly lower tariffs hn other exports in 1990. This difference was particularly large for countries which had GSP status-Hungary, Poland and Romamia. The proportion of export affected by NTBs was also substantially lower for industrial products; the NTB coverage ratio for "other" exports was around double that for industial exports.

Although tariffs have lost much of their protective significance, the margins of preference between CEE-5 exporters and those who did not have GSP status were quite large. On average, MEN rates imposed in 1990 on inports from Bulgaria and the FCSK were around 7 percent as compared with GSP rates of 0.1 percent on imports from the other CEE-5. GSP staus sigificantly improved CEE-5 access to EC markets, and its short-erm impact on exports was larger than that of the ITAs (Inotai, 1993). As can be seen from Table 2, GSP status does not have much significance for non-industial products.
Comparing responses between the 2007 and 2011-12 surveys and attributing the changes observed to the crisis can be problematic because we cannot be certain that the changes observed were indeed triggered by the crisis. Hence, to investigate the changes that were directly related to the crisis, the 2011-12 survey includes questions that specifically ask regulators to identify reforms introduced in response to the crisis.

Table 4 lists these questions, the responses provided by crisis and non-crisis countries, and the p-values from tests of the null that the changes observed across countries are not significant. The responses confirm the changes in regulations pertaining to bank governance and deposit insurance highlighted above. Furthermore, Table 4 also suggests that bank resolution was also an area of reform after the crisis.

Interestingly, regulatory changes during the global financial crisis did not seem to differ significantly (with some exceptions) between countries that were directly hit during the global financial crisis and those that avoided the direct crisis impact (Table 4).<sup>6</sup> In the area of bank governance, crisis-hit countries were more than twice as likely as non-crisis countries to introduce new requirements on executive compensation (Table 4). In the area of deposit protection, we observe no significant differences. Similarly, in the area of bank resolution framework, the differences are not very strong.

Overall, while some of these reforms are encouraging, the survey suggests that the regulatory response to the crisis has been slow, and that there is room for further improving the regulatory and supervisory frameworks as well as private incentives to monitor risk-taking.

> 6 The analysis in this section uses the same definition of “crisis” and “non-crisis” countries as used in the preceding section.

12
take several months to adopt it. A major reason is that the owners were severely time constrained, working an average of 68 hours per week already. There was also evidence of procrastination in that some owners would defer on taking quick decisions. This matches up with the evidence on procrastination in other contexts, for example African farmers investing in fertilizer (Duflo, Kremer and Robinson, 2009).

Finally, somewhat surprisingly, we did not find evidence for the direct impact of capital constraints, which are a significant obstacle to the expansion of micro-enterprises (e.g. De Mel et al., 2008). Our evidence suggested that these large firms were not cash-constrained, at least for tangible investments. We collected data on all the investments for our 17 firms over the period August 2008 until August 2010 and found the firms invested a mean (median) of $880,000 ($140,000). For example, several of the firms were adding machines or opening new factories, apparently often financed by bank loans. Certainly, this scale of investment suggests that investment on the scale of $2000 (the first-year costs of these management changes, ignoring the consultants’ fees) is unlikely to be directly impeded by financial constraints.

Of course financial constraints could impede hiring international consultants. The market cost of our free consulting would be at least $250,000, and as an intangible investment it would be difficult to collateralize. Hence, while financial constraints do not appear to directly block the implantation of better management practices, they may hinder firms’ ability to improve their management using external consultants. On the other hand, our estimates of the return on hiring consultants to improve management practices suggest profitability in just over one year.

# **_VII.C. How do badly managed firms survive?_**

We have shown that management matters, with improvements in management practices improving plant-level outcomes. One response from economists might then be to argue that poor management can at most be a short-run problem, since in the long run better managed firms should take over the market. Yet many of our firms have been in business for 20 years and more.

One reason why better run firms do not dominate the market is constraints on growth derived from limited managerial span of control. In every firm in our sample only members of the owning family have positions with major decision-making power over finance, purchasing, operations or employment. Non-family members are given only lower-level managerial positions with authority only over basic day-to-day activities. The principal reason is that family members 27
annual cost of $18,000. PAHO also includes annual in-service physician training at a cost of about $350 per year.

The PAHO and Columbia studies provide estimates of the cost of equipping this level of facility. PAHO estimates that the cost of equipping a physician's office for prenatal monitoring would be $3,000. Co' Ambia estimates that health center supplies and equipment would cost about $7,000. Of course, these two facilities may be providing different levels of care, and are not strictly comparable.

At the hospital level, costs begin to rise substantially in all three studies. Herz and Measham estimate that hospital staff time for Safe Motherhood would cost about $15,000 per year. They also Include $15,000 per year to equip and supply an operating room, and discuss additional the training needed for caesareans, sterilization, etc., but it is difficult to determine the unit costs.

These estimates are considerably lower than those of the Columbia study, which includes:

|First referral facility (small rural hospital)||
|---|---|
|10 midwives at $300 per month|$36,000|
|<br>1 physician at $10,000 per year|10,000|
|Supplies and equipment|20.000|
|TOTAL|$66,000|

PAHO does not include the cost of staff time, but provides a detailed breakdown of the cost of equipment at this level: Equipment for obstetric services at first level of referral

|Anesthesia equipment|$12,000|
|---|---|
|D&C kit|350|
|Minilaparatomy kit|200|
|Surgical instruments|7,900|
|Lab and blood bank equipment|8.500|
|TOTAL EQUIPMENT COST|$28,950|

There are several other aspects of these programs for which the assumptions underlying unit costs are not clear. Herz and Measham, for example, provide the cost of health education and of monitoring and evaluation without indicating the basis for their calculations. Other examples include:

# From PAHO Regional Plan of Action:

|Stay in maternity home|$48|
|---|---|
|Delivery in low-risk birthing center|20|
|Normal delivery in birthing home|200|

26
an electric grid by 10 km marginally increases employment by 4 percentage points in the younger age group and 15 percentage points in the older age group in the LSMS sample; the corresponding estimates for the DHS sample are 9 percentage points for the younger age group and -2 percentage points for the older age group. The joint effect of roads and electricity is positive and statistically significant for both age groups in the LSMS sample, with a greater magnitude for the older group. By contrast, in the DHS sample, the effect is significant only among those in the younger group.

The OLS results are reported in Table 13 and largely mirror the findings from the IV results, with a few differences. However, overall, both the OLS and the IV estimates show that expanded access to roads and electricity affect employment mostly among young and middle-aged individuals. These findings therefore suggest that younger individuals have a greater ability to benefit from the expansion of these types of infrastructure, most likely because people in these groups can more easily change occupations.

# **7 Conclusions**

Using two sources of geo-referenced household surveys and spatial data on road and electricity expansion in sub-Saharan Africa, we analyze the average and heterogeneous impacts on job creation that stem from investments in roads and electrification. We examine the impacts that stem from expanding access to road networks and electric grids, both in isolation and in combination. We find that significant, positive complementarity effects emerge between both types of investments. The positive impacts are heterogeneous across countries, locations, gender, and age groups. The gains from investing in roads and electricity grids tend to be greater in more-advanced economies, where the complementarities are greater. Within countries, the benefits of expanding the reach of such infrastructure differ between urban and rural areas. Employment gains from roads accrue to a greater degree in rural areas. Moreover, the complementarity between the two types of infrastructure is much stronger in rural areas. At the same time, the expansion of such infrastructure leads to a decline in low-skilled employment, including in agricultural jobs, as people move into other types of work requiring higher skills. In general, though both women and men benefit from better access to roads and electricity, women benefit more. The employment gains from better infrastructure access tend to be greater for younger individuals. Our findings imply that roads and electricity play important roles in the structural transformation of sub-Saharan Africa. The continent is undergoing the same transition away from low-productivity agriculture and toward other occupations as other parts of the world.

29