rafmacalaba/datause-encoder-data huggingface dataset:
|Water SupplyProject|Jan G. Janssens|
|---|---|
|Water SupplyProject|Jan G. Janssens|
|Water SupplyProject|Jan G. Janssens|
|Water SupplyProject|Jan G. Janssens|
|Water SupplyProject|Jan G. Janssens|
|Water SupplyProject|Jan G. Janssens|
|Water SupplyProject<br>|Jan G. Janssens<br>|
|Construction Capacityand Employment Project|Abdelghani Inal|
|Construction Capacityand Employment Project|Abdelghani Inal|
|Construction Capacityand Employment Project|Abdelghani Inal|
|Construction Capacityand Employment Project|Abdelghani Inal|
|Construction Capacityand Employment Project|Abdelghani Inal|
|Construction Capacityand Employment Project|Abdelghani Inal|
|MR-PE Sec AdjDPL(DR)|Francois Rantrua|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Technical Education and Vocational TrainingSupport Project|MercyMiyangTembon|
|Public Resource Management Credit Project|Miguel J. Saponara|
|Public Resource Management Credit Project|Miguel J. Saponara|
|Public Resource Management Credit Project|Miguel J. Saponara|
|Public Resource Management Credit Project|Miguel J. Saponara|
|Public Resource Management Credit Project|Miguel J. Saponara|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|Rainfed Natural Resource Management Project(NRMP)|Amadou Oumar Ba|
|SUGAR ENERGY BIOTECH|Yves WongYou Cheong|
|SUGAR ENERGY BIOTECH|Yves WongYou Cheong|
|Education Project(02)||
|Power Transmission Project(02)||
|Development Bank of Mauritius Project(05)||
|Development Bank of Mauritius Project(05)||
|Development Bank of Mauritius Project(05)||
|Urban Development and Rehabilitation Project||
|Urban Development and Rehabilitation Project||
|Urban Development and Rehabilitation Project||
|Development Bank of Mauritius Project(06)||
|Development Bank of Mauritius Project(06)||
||**Ta**|**ble A6: Depen**|**dence of secto**|**rs on female**|**workers**|||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Female|0.586***<br>(0.220)|0.535**<br>(0.216)|0.471**<br>(0.214)|0.526**<br>(0.207)|0.418*<br>(0.223)|0.418*<br>(0.219)|0.403*<br>(0.215)|
|Dependence of<br>Sectors||||||||
|Exports (%, cell<br>averae)|-0.055|-0.025|-0.006|-0.024|0.039|0.009|-0.718|
|g<br>Female Dependence<br>of Sectors|(0.101)<br>43.106***|(0.100)<br>43.900***|(0.101)<br>45.375***|(0.103)<br>45.061***|(0.109)<br>48.872***|(0.092)<br>48.382***|(0.449)<br>41.236***|
||(5.772)|(5.753)|(5.709)|(5.882)|(6.343)|(6.366)|(9.237)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.081|
||||||||(0.053)|
|No. of Workers<br>(logs)*Female<br>Dependence of|||||||2.199|
|Sectors|||||||(1.897)|
|No. of Workers (logs)||0.605**|0.800***|0.827***|0.971***|0.977***|0.242|
|||(0.258)|(0.271)|(0.297)|(0.310)|(0.308)|(0.638)|
|GDP per capita (logs)||15.497***|14.906**|13.987**|10.334|1.297|-0.696|
|||(5.678)|(5.965)|(6.141)|(6.548)|(6.179)|(6.428)|
|Herfindahl-Hirschman|||-1.272|0.690|-1.336|-0.835|-1.032|
|Index||||||||
||||(3.955)|(4.029)|(4.759)|(4.327)|(4.324)|
|Age of Firm (logs)|||-0.434|-0.873**|-0.586|-0.736*|-0.705*|
||||(0.381)|(0.362)|(0.386)|(0.383)|(0.383)|
|Foreign Ownership<br>(proportion)|||-1.549*|-0.670|-0.671|-0.569|-0.623|
||||(0.881)|(0.934)|(1.028)|(1.021)|(1.017)|
|Female Ownership<br>Y:1 N:0||||6.154***|5.981***|5.944***|5.982***|
|||||(0.669)|(0.713)|(0.710)|(0.710)|
|Skills Obstacle (0-4)||||0.664***|0.769***|0.740**|0.745**|
|||||(0.250)|(0.293)|(0.293)|(0.292)|
|How Much Of An||||<br>-0.046|<br>-0.011|<br>-0.036|<br>-0.042|
|Obstacle: Transport?||||||||
|||||(0.205)|(0.223)|(0.223)|(0.223)|
|Firm Purchased Fixed<br>Assets Y:1 N:0||||-0.770|-0.658|-0.606|-0.587|

61
|Negative decision on definitive<br>measures||1|2|1|2||3|
|---|---|---|---|---|---|---|---|
|Presumed negative|1||2||1||3|
|Ongoing||||||3||

_Source:_ Tabulated from information in WTO “Report [one per year 2000-4]of the Committee on Safeguards to the Council for Trade in Goods” for 2000 2004 _Note:_ The actions are tabulated by the year in which each investigation was initiated, for example, if an Argentine investigation initiated in 1997 led to a definitive measure put in place in 1998, then that definitive measure is included in the tabulation for 1997, not for 1998.

The countries in the table all notified zero actions in 1995, the first year the WTO notification requirement was in force.

The WTO reports no notification of measure taken or of termination for several investigations that have past the WTO deadline for a final determination. We have labeled these “outcomes” as “presumed negative.” 54
# **Table 1.   Results for Baseline Estimation of Equation [1]**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Variables|VA per worker|VA per worker|VA|Changes VA per<br>worker|VA per worker|VA per worker|GMM estimates|
|Capital|0.32***|0.30***|0.33***|0.32***|0.32***|0.30***|0.31***|
||(0.032)|(0.032)|(0.038)|(0.041)|(0.032)|(0.032)|(0.042)|
|**RD&D**|**0.015*****|**0.0088****|**0.0083****|**0.0067***|||0.097**|
||**(0.003)**|**(0.0035)**|**(0.0035)**|**(0.0037)**|||(0.048)|
|Labor|0.062**|-0.26***|0.30***|-0.027|0.068**|-0.20***|-0.014|
||(0.028)|(0.045)|(0.11)|(0.048)|(0.028)|(0.048)|(0.048)|
|**R&D**|||||**0.017*****|**0.074*****||
||||||**(0.003)**|**(0.019)**||
|Dummy 2015|0.17***|0.18***|0.18***||0.18***|0.18***||
||(0.014)|(0.015)|(0.016)||(0.013)|(0.015)||
|Dummy 2016|0.35***|0.35***|0.33***||0.34***|0.34***||
||(0.021)|(0.023)|(0.026)||(0.021)|(0.023)||
|Missing dummy<sup>a</sup>|0.061*|0.090*|0.080|0.048**|0.068*|0.85***|0.066**|
||(0.033)|(0.048)|(0.048)|(0.024)|(0.037)|(0.23)|(0.029)|
|Constant|9.26***|10.6***|10.7***|0.33***|9.27***|10.4***|0.30***|
||(0.29)|(0.38)|(0.40)|(0.027)|(0.28)|(0.38)|(0.028)|
|Observations|9,246|9,246|9,254|2,949|9,246|9,246|2,949|
|R-squared|0.159|0.390|0.397|0.055|0.158|0.393||
|Number of ID|3,489|3,489|3,490||3,489|3,489||
|Firm FE|No|Yes|Yes|No|No|Yes|No|

_Note:_ FE = fixed effects; GMM = generalized method of moments; R&D = research and development; RD&D = research design and development; VA = value added. Standard errors are clustered at the sector-size level.

*** p<0.01, ** p<0.05, * p<0.1.

- a. We replaced missing values of investment in R&D and Industrial Design with  zero in order to avoid losing observations. We control for this underestimation of our stock of knowledge variable by including a dummy that indicates whether the firm reported missing values in any of these categories or not. The estimated coefficient has the expected sign and is almost always statistically significant.

20
government-run CDD programs, these savings can be significant. For example, in the big Indonesia National Community Empowerment Program, a 25 percent savings in a billion dollar annual appropriation translated into USD 250 million more becoming available to the government for its poverty work.

Studies on Economic Rates of Return (ERR) of these CDD programs also show levels above the country ’ s hurdle rate for acceptable investments. Weighted ERR levels for the Philippines, Indonesia, and Burkina Faso, for example, demonstrated relatively high levels of economic impact ranging from 21 to 86 percent. The India livelihood projects were showing internal rates of return for various livelihood activities of 40 percent and higher.

For roads, benefits are seen through improved connectivity, savings in travel time, more frequent market trips, more motorized traffic, increased passenger movement, increased labor and agriculture machinery movement, and reduced unit costs of transport. Large benefits accrued from entirely new economic activities and access to markets that were made possible by transport infrastructure. For water projects, the returns come primarily from time savings.

Furthermore, CDD programs in Asia, Africa, and Latin America have provided hundreds of millions of poor villagers and self-help groups, primarily poor women, with much-needed credit services, albeit with ongoing controversies over their sustainability. More advanced programs, particularly in South Asia, have coupled microcredit access with small business management training and advances in marketing, insurance, and village-level procurement.

**Going to Scale and Replicability** . As mentioned in the earlier section, countries have succeeded in scaling-up these programs in a significant way. The CDD model is not limited by geographical scope, and its modular and flexible design allows for regional variation and “franchising”.  While a CDD program can take many shapes and forms adapted to the local context, the more successful examples keep community demand and poor people as the center of program design.

**National Ownership.** CDD programs often turn out to be enormously popular. For national governments, they allow tangible, visible public goods and services to be delivered to its citizens quickly and transparently. For local governments that often provide counterpart contributions, they also serve to meet the local needs of its citizenry in tangible ways.  And for local communities, after years if not decades of receiving few if any services,

17
# **Figure 2.  Corruption and bribes**

<!-- Start of picture text --> Corruption<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>B GD<br>AFG<br>PAK<br>MDV<br>NPL IND<br>LKA<br>BTN<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>Bribes (>0)<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>BGD<br>PAK<br>AFG MDV<br>IND<br>LKA<br>NPL BTN<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> 66
||**Scenario**|**H**<br>**Rank co**|**A**<br>**rrelation (**|**M0**<br>**k=33%)**|**Partial**<br>**Dom**|**Full**<br>**inance**|
|---|---|---|---|---|---|---|
|**Change in the definition of**<br>**indicators**|Indicator considered "Level of education".<br>Baseline is complete secondary education.<br>Alternative is completeprimaryeducation.|0.98***|0.98***|0.99***||X|
||Weight of Social Inclusion 40%, all others 20%.|0.93***|0.95***|0.96***|X||
|**Changes in weights: 1**<br>**dimension gets 40% all**|Weight of Resilience 40%, all others 20%.|0.92***|0.85***|0.9***|X||
|<br>**others 20%**|Weight of Social Cohesion 40%, all others 20%.|0.85***|0.65***|0.85***|X||
||Weight of Process Legitimacy 40%, all others<br>20%.|0.24|0.35*|0.29|X||
||Quality of employment dropped; weights<br>reassigned from 1/16 to 1/12.|0.94***|0.97***|0.98***|X||
||Access to water, sanitation, electricity & internet<br>dropped; weights reassigned from 1/16 to 1/12.|0.91***|0.89***|0.95***|X||
||Level of education dropped; weights reassigned<br>from 1/16 to 1/12.|0.95***|0.93***|0.98***|X||
||Medical attention dropped; weights reassigned<br>from 1/16 to 1/12.|0.93***|0.98***|0.96***|X||
||Quality of housing dropped; weights reassigned<br>from 1/16 to 1/12.|0.95***|0.77***|0.98***|X||
||Possession of assets dropped; weights reassigned<br>from 1/16 to 1/12.|0.93***|0.82***|0.97***|X||
||Public assistance dropped; weights reassigned<br>from 1/16 to 1/12.|0.89***|0.93***|0.94***|X||
|**Changes in the number of**<br>**indicators per dimension.**|Capacity for saving dropped; weights reassigned<br>from 1/16 to 1/12.|0.92***|0.86***|0.94***|X||
|**We drop one indicator at a**<br>**time: from 4 to 3 indicators.**|Confidence in government institutions dropped;<br>weights reassigned from 1/16 to 1/12.|0.96***|0.94***|0.97***|X||
||Experience of discrimination dropped; weights<br>reassigned from 1/16 to 1/12.|0.96***|0.98***|0.97***|X||
||Perception of safety dropped; weights reassigned<br>from 1/16 to 1/12.|0.95***|0.98***|0.97***|X||
||Victim of crime dropped; weights reassigned<br>from 1/16 to 1/12.|0.94***|0.98***|0.97***|X||
||Civil participation dropped; weights reassigned<br>from 1/16 to 1/12.|0.94***|0.92***|0.93***|X||
||Satisfaction with democracy dropped; weights<br>reassigned from 1/16 to 1/12.|0.95***|0.9***|0.95***|X||
||Government effectiveness dropped; weights<br>reassigned from 1/16 to 1/12.|0.95***|0.91***|0.96***|X||
||Equality before the law dropped; weights<br>reassigned from 1/16 to 1/12.|0.96***|0.88***|0.95***|X||

1Statistical significance levels: *p<0.1, **p<0.05, ***p<0.01. Spearman's rank correlation coefficients are used throughout.

25
<!-- Start of picture text --> unemployment and preference for safe jobs unemployment and preference for 'important work'<br>0 5 10 15 20 0 5 10 15 20<br>Unemployment, male (% of male labor force) Unemployment, male (% of male labor force)<br>(mean) safe_job Fitted values (mean) imp_job Fitted values<br>Source: WVS 2005-7 and WDI 2005 data Source: WVS 2005-7 and WDI 2005 data<br>.5<br>.5<br>.4<br>.4<br>.3<br>.3<br>.2<br>.2<br>.1<br>.1<br><!-- End of picture text --> Source WVS wave 5, full sample, selected responses to question v48 The WVS is not a dedicated labor force survey and there are some concerns about the quality of the definition of underlying employment categories.<sup>14</sup> For example unemployment may be measured with higher measurement error in lower income countries or unemployment may be a less meaningful category in settings where poorer households cannot afford to forgo income generating activities.

However, in spite of these caveats we observe interesting cross-country patterns in the association between life satisfaction and employment status when we turn to individual-level regression estimates (Table 2). In the case of unemployment we see a relatively clear gradation across countries at different income levels. The coefficient of unemployment is largest in high income countries (-0.64) and it is reduced by a comparatively small amount (to -0.51) as one moves to high and middle income nations (Panel A). The coefficient then drops by almost two-thirds in lower middle income countries (-0.18). In low income countries, here exclusively represented by economies in Sub-Saharan Africa, the effect of unemployment becomes entirely insignificant and the sign of the coefficient actually reverses.

> 14 To address these issues we also estimate the wellbeing effect of unemployment using data from the 2006 European Bank Reconstruction and Development’s Life in Transition Survey (LITs). Among several value and perception surveys reviewed for this study, the LITs has the most detailed information on unemployment and job histories. We used this information to carry out robustness tests with alternative definitions of unemployment and more detailed controls for other job categories. These tests did not generate qualitatively different results, relative to the findings reported below. Results are available on request from the authors.

14
estimates among all commodities (0.54, the third highest after natural gas and silver). Between 1956 and 1985, the tin market was subjected to export controls through successive International Tin Agreements (Anderson and Gilbert 1988). Therefore, the comovement between the price of crude oil (which is subject to a cartel as well, OPEC), at least for 1972-85, may not be a market-based outcome but rather a result of the actions by the Tin Agreement and OPEC; i.e., the conditions that allowed OPEC to be in a position to exert power were the same as those of the Tin Agreement, hence forcing prices to move in the same direction (note that following the collapse of the Tin Agreement, tin prices fell by more than 40 percent.)<sup>**10**</sup>

# **Concluding remarks**

Based on annual data from 1960 to 2005 and a simple econometric model, this paper estimates the degree of pass-through of crude oil price changes to the prices of 35 other internationally-traded primary commodities. The elasticity for the non-energy commodity index was estimated at 0.16. At a more disaggregated level, the fertilizer index exhibited the largest pass-through, followed by the index for food commodities. Precious metals (not part of the non-energy index) had a very high pass-through, while beverages, raw materials, and metals gave a mixed picture. The implications are that if crude oil prices remain high, as most analysts believe will happen, then the recent commodity price boom is likely to last much longer than earlier booms, at least for the food commodities, fertilizers, and precious metals.<sup>**11**</sup> The other commodities however, especially metals and raw materials, are likely to follow diverging paths. On the methodological side, the results showed that price indices, while providing useful summary statistics, ought to be supplemented by individual commodity analysis.

Among the various ways of extending the current model (which may include the use of higher frequency data or the incorporation of dynamics through an errorcorrection mechanism), a useful extension would be to include additional explanatory

**_— 10 —_**
analyzed in the following are inventory size, supply network size, generator ownership, water tank ownership, and investment volumes. If is continuous—for example, in the case of firms’ inventory size—ordinary least square regressions are carried out, where yy . Here, is the vector of explanatory variables; is a set of controls; and is the error term. All regressions yy= XX<sup>′</sup> ββ+ ZZ<sup>′</sup> θθ+ εε take the survey design, stratification, and respective sampling weights into account.<sup>1</sup> Estimates are hence XX ZZ εε representative for the overall population of firms in Dar es Salaam, Dodoma, and Tanga. As the sample size is relatively small compared to the overall population size (about 1.4 percent of the NBS registry), no finite population correction is employed. Standard errors are clustered at the ward level. If is binary—for example, in the case of firm generator ownership—a logit model is used, where εε yy All variables are defined as before and is the logit link function, taking the form PP(yy= 1) = EE[yy] = gg(XX′ββ+ ZZ′θθ). gg ~~.~~ exp(ηη) As before, appropriate sampling weights and stratification are considered. gg(ηη) =<sup>2</sup> If data on dependent or exp(ηη) + 1 As before, appropriate sampling weights and stratification are considered.<sup>2</sup> If data on dependent or independent variables for specific firms was missing—that is, there was no response to the survey—these firms were excluded from the respective regressions. Some parts of the questionnaire were only posed to relevant firms— for example, questions on water tank ownership were only posed to firms that require water to run their business. As a result, some regressions rely on sample sizes below the survey sample size of 837, reducing the robustness of statistical estimates. We note that the survey design in combination with the above statistical techniques do not allow us to confirm causal effects. Surveyed measures of disaster exposure are likely to suffer from measurement error. Further, estimates are prone to omitted variable bias resulting in endogeneity issues. Nevertheless, the GLM methodology allows us to analyze correlations, which can be suggestive for potential coping mechanisms at work.

> 1 Here, the weights are excluded from the regression formula for simplicity.

> 2 Estimates and from the logit model cannot be interpreted as estimates from a linear probability model: does not indicate the marginal effect of changes in on , but indicates the marginal effect on the log odds jj ββ, γγ θθy ββ ratio—that is, log � ~~�~~ . To obtain easily interpretable results, mean marginal effects are reported when logistic jj PP( y=1) XX PP(yy= 1) regressions are employed. The mean marginal effect indicates the average effect for a unit change in on PP(yy=0) 1) across all firms. Indexing individual firms with and the total number of firms the mean marginal effect can 1 XXjj PP(yy= hence be calculated as ∑<sup>∂∂PP(y</sup> suppressing sampling weights for simplicity.

> <sup><u>i</u></sup> i<sup>=1)</sup> ii nn, ii nn ∂∂XX

8
Table 12 reports the estimates for various specifications of regression (7). Column 1 includes as technology measures the average level of EXT at the firm level, while column 3 includes the average level of MOST. The main finding is that, for both measures, the coefficient of technology on productivity is higher for agricultural firms than for non-agricultural firms. This difference in the coefficients together with the greater cross-country gap in average technology differences in agriculture than non-agriculture firms opens the possibility for technology to account for some of the sectoral variation in the cross-country productivity gap.

Table 12: Productivity and Technology Sophistication, Industry-heterogeneity

|VARIABLES|(1)<br>ln(VAPW)|(2)<br>ln(VAPW)|(3)<br>ln(VAPW)|(4)<br>ln(VAPW)|(5)<br>ln(VAPW)|
|---|---|---|---|---|---|
|ABF EXT*AGRI|0.95***<br>(0.20)|0.84***<br>(0.19)||||
|ABF EXT*NO AGRI|0.64***<br>(0.07)|0.56***<br>(0.07)||||
|ABF MOST*AGRI|||1.18***|1.07***|1.05***|
||||(0.30)|(0.31)|(0.36)|
|ABF MOST*NO AGRI|||0.72***|0.67***|0.67***|
||||(0.10)|(0.09)|(0.09)|
|ABF GAP*AGRI|||||0.53<br>(0.60)|
|ABF GAP*NO AGRI|||||0.44***<br>(0.11)|
|Var(ABF EXT)*AGRI||0.35||0.41|0.33|
|||(0.30)||(0.34)|(0.30)|
|Var(ABF EXT)*NO AGRI||0.38***||0.51***|0.40***|
|||(0.10)||(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|2,746|2,746|2,746|
|R-squared|0.49|0.49|0.47|0.49|0.49|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

The remaining columns in Table 12 and Table 13 explore the robustness of this key finding. Columns 2, 4 and 5 of Table 12 include as control the within-firm variance in technology. Column 5, in addition, introduces the average level of GAP in the firm. In all 39
> - L1-

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