rafmacalaba/datause-encoder-data huggingface dataset:
16 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity complementary since they allow differentiating between an irregularly compact city and a smoothly compact city, the latter being closer to a perfect circle.

_Fullness_ measures the presence of built-up areas within the urban extent as a fraction of the urban extent area. We used the GHS built-up 1990 raster layer at 250 meters resolution (GHS_BUILT_LDS1990_GLOBE_R2016A_54009_250, Pesaresi et al., 2015; Pesaresi et al., 2016) to calculate this variable. The GHS built-up layer values are expressed as decimals from 0 to 1, and correspond to the fraction of the pixel that is covered by a building (Pesaresi et al., 2016). Fullness was measured as the mean value of all the pixels of the GHS built-up 1990 layer within the urban extent. This measure can be understood as the complement of the sprawl index proposed by Burchfield et al. (2006) defined as the measure of absence of built-up area. Based on the extensive literature on the relationship between compact cities and productivity, we would expect a “full-city” to be one that is also a more compact city, hence allowing for greater interaction and therefore higher agglomeration economies which ultimately increase productivity. However, a city that is “too-full” might also be suggestive of lack of public space, which can both be a dissamenity and reflect lack of planning. For these reasons we can expect a non-linear relationship between _fullness_ and productivity.

Following the above-mentioned rationale, our hypotheses regarding the effect of these indicators of city shape on productivity are as follows:

- **H1** : An increase of the _Exchange_index_ suggests a more circular urban extent, which is expected to be associated with higher productivity levels of the city.

- **H2:** An increase of the _Perimeter_index_ implies smooth perimeters that are associated with higher productivity levels of the city.

- **H3** : An increase of the _Fullness_index_ implies a less fragmented urban layout that increases productivity levels of the city, but at extremely high levels of fullness can affect productivity.

As mentioned in Section 2, the internal structure of the city may play a considerable role in its productivity levels. Natural/organic cities have different dynamics than planned/regular/artificial/geometric cities; thus, it is important to include variables that capture the different urban structures in order to quantify its impact on productivity. Such structures affect the way in which people and products move within the city. Previous
> - 16 -

begins with the formation of a high-level interministerial commission or task force examining questions of pay and employment, often within the context of larger organizational issues in the public administration. (Zambia, Malawi, Bangladesh, Senegal, Sri Lanka, CAR, and Ghana, among others, all had such commissions.) These commissions may issue reports and organize or oversee civil service reform. They may be short-lived, as in Bangladesh, or become more fully institutionalized, as in Senegal. They may or may not offer remuneration to their members. The nature of these commissions and their impact on reform varies. In Bangladesh a national pay commission meets regularly to review salary policy and in 1983, a public administration commission, (ENAM) reorganized the public service and instituted a far-reaching decentralization program; another high-level committee is now about to review the functions and effectiveness of the management and professional levels of the public service. In Sri Lanka the National Salary Commission, a standing body, substantially raised salaries for higher level staff, prodded by a Special Presidential Administrative Reforms Committee.

The actual implementation of employment and pay policy reform takes place in a wide variety of settings within governments. More often than not, personnel management and pay functions are divided between the Ministry of the Civil Service and the Ministry of Finance. Personnel management functions are also carried out in the personnel branches of technical ministries as well as in local decentralized units of government. Coordination and reconciliation of procedures as well as records is required among these various agencies. In some cases, an additional entity to carry out system-wide reforms is grafted onto one of these or is attached to the president's or prime minister's office. (Examples of these reform entities are found in Mali, Mauritania, Central African Republic, Guinea, and Senegal, among others.) They are sometimes staffed with expatriate consultants, as in the case of a Bank-UNDP, co-financed administrative reform project in Mali. In Senegal, the institutional proliferation of agencies making policy on employment and pay issues has produced poorly coordinated decisions and contradictory staff rules. Past efforts at reform have also been ineffective, partly because they have consistently come from central agencies outside the technical ministries.11 Nonetheless, reform efforts located in more autonomous institutions with greater access to higher levels of government generally have a better chance of succeeding. In the Central African Republic, for example, the National Commission for Administrative Reform was first placed under the Ministry of the Civil Service which lacked the political influence and independence to carry out the difficult choices involved in making employment cuts. The Commission was subsequently moved to the Office of the President to afford the reform progrem greater legitimacy and political leverage.

_j/ John Nellis, An Assessment of the Senegalese Administrative System, WA2DB Working Paper/Reports No. 7, September 11, 1986.
**Table 5.1: Program targeting: JEEVIKA**

|(a) Government caste ca|tegories|(b) SC/ST jatis||(c) Non SC/ST jatis||
|---|---|---|---|---|---|
|ST|-0.160|SC: Chamar|0.140***|OBC: Dhanuk|-0.122|
||(0.089)||(0.024)||(0.075)|
|OBC|-0.078***|SC: Dobha/Dobh|0.048|OBC: Koeri|-0.104|
||(0.023)||(0.053)||(0.073)|
|EBC|-0.076*|SC: Dom|-0.235*|OBC: Kurmi|-0.185*|
||(0.037)||(0.102)||(0.074)|
|Muslim|-0.170***|SC: Dushad|0.179***|OBC: Shershabadia|-0.700***|
||(0.042)||(0.024)||(0.092)|
|FC|-0.300***|SC: Musahar|0.061*|OBC: Yadav|-0.044|
||(0.039)||(0.024)||(0.033)|
|||SC: Pasi|0.077|Other OBCs|-0.023|
||||(0.077)||(0.035)|
|||SC: Sardar|0.044|EBC: Keuta|-0.041|
||||(0.072)||(0.115)|
|||Other SCs|-0.352***|EBC: Mallah|-0.194*|
||||(0.091)||(0.089)|
|||ST: Adivasi|-0.028|EBC: Nat|-0.023|
||||(0.105)||(0.091)|
|||Other STs|-0.175|Other EBCs|-0.057|
||||(0.161)||(0.046)|
|||||FC: Brahmin|-0.195**|
||||||(0.062)|
|||||FC: Rajput|-0.402***|
||||||(0.045)|
|||||Other FCs|-0.266**|
||||||(0.087)|
|||||Muslims: Ansari|-0.379**|
||||||(0.123)|
|Some Schooling|-0.120**|Some Schooling|-0.150***|Some Schooling|-0.124**|
||(0.043)||(0.043)||(-0.043)|
|Female headed|0.045*|Female headed|0.038|Female headed|0.043|
|household|(0.022)|household|(0.023)|household|(0.022)|
|Per capita expenditure|0.118|Per capita expenditure|0.114|Per capita expenditure|0.108|
||(0.103)||(0.103)||(0.102)|
|Per capita expenditure|-0.037|Per capita expenditure|-0.040|Per capita expenditure|-0.030|
|squared|(0.053)|squared|(0.054)|squared|(0.053)|
|Land|-0.017***|Land|-0.020***|Land|-0.019***|
||(0.005)||(0.005)||(0.005)|
|Observations|4187|Observations|4187|Observations|4187|
|Adjusted R-squared|0.130|Adjusted R-squared|0.136|Adjusted R-squared|0.136|

Notes: (1) SC is the omitted caste group in column (a), Non-SC/ST jatis are the omitted group in column (b) and SC/ST jatis are the omitted group in column (c). (2) Each column represents a separate regression wherein household's participation in NREGA is regressed on caste identity variables and a set of controls: age, age squared, marital status, age at marriage, a dummy variable for any schooling; a dummy variable for household female-headship, per-capital expenditure, per-capita expenditure squared, landholdings, education of the household head, number of members in the household and panchayat level fixed-effects; (3) Robust standard errors are in brackets. (4) We report the level of significance: * p value < .05 , ** p value < .01 and *** p value < .001.

17
**Figure 4: Operational Stock of Robots per 1000 Employees in HICs, by sector, 2004-2015**

<!-- Start of picture text --> 5.00<br>4.50<br>Motor vehicles and other transport<br>equipment<br>4.00 Electrical/electronics<br>3.50 Rubber and plastics products, and other<br>non‐metallic mineral products<br>3.00 Metals and metal products<br>Industrial machinery<br>2.50<br>Food and beverages<br>2.00<br>All other manufacturing branches<br>1.50<br>Wood and furniture<br>1.00<br>Textiles, apparel, and leather products<br>0.50<br>0.00<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: Calculations based on fDi Markets Database and International Federation of Robotics Database

**Figure 5: Ratio of Robot Stock per 1000 Employees in Electronics to apparel in HICs and Ratio of Cumulative FDI Flows in Electronics to apparel from HICs to LMICs, 2003-2015**

<!-- Start of picture text --> 10 1.6<br>9<br>1.4<br>8<br>1.2<br>7<br>1<br>6<br>5 0.8<br>4<br>0.6<br>3<br>0.4<br>2<br>0.2<br>1<br>0 0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Robots per 1000 employees in electronics relative to apparel<br>Cumulative FDI flows from HIC to LMICs in electronics relative to apparel<br><!-- End of picture text --> Source: Calculations based on fDi Markets Database and International Federation of Robotics Database 9
Policy Research Working Paper 5646

# **Abstract**

The authors use data from more than 6,000 World Bank projects evaluated between 1983 and 2009 to investigate macro and micro correlates of project outcomes. They find that country-level “macro” measures of the quality of policies and institutions are very strongly correlated with project outcomes, confirming the importance of countrylevel performance for the effective use of aid resources. However, a striking feature of the data is that the success of individual development projects varies much more _within_ countries than it does between countries. The authors assemble a large set of project-level “micro” correlates of project outcomes in an effort to explain some of this within-country variation. They find that measures of project size, the extent of project supervision, and evaluation lags are all significantly correlated with project outcomes, as are early-warning indicators that flag problematic projects during the implementation stage. They also find that measures of World Bank project task manager quality matter significantly for the ultimate outcome of projects. They discuss the implications of these findings for donor policies aimed at aid effectiveness.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akraay@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
biases resulting from country level confounders correlated with both water and sanitation and child mortality we also control for survey fixed effects.<sup>13</sup> In a second step, we use the estimated relative mortality reductions to compute the predicted absolute mortality reductions per 1000 live births which would be generated by improving water and sanitation infrastructure under two scenarios: In the first scenario, we assume that eliminating the lack of access to improved water and sanitation is the policy target. Under this “low cost” scenario, we assume that any household currently using surface or unprotected water sources would be upgraded to an improved water source, and any household using open defecation or only unimproved sanitation technologies would get an improved latrine. The second scenario reflects a more ambitious program aiming at upgrading water and sanitation access to the highest possible standard. In this scenario, any household that does not already have access to privately piped water and a toilet connected to a septic tank or sewage system will be provided with such access.

The main question we want to address in this paper is the potential contribution of water and sanitation improvements towards achieving MDG 4. MDG 4 aims at reducing child mortality by two thirds in each country between 1990 and 2015. Based on the 1990 child mortality levels (World Population Prospects, United Nations Population Division), we first assign an under-5 mortality target for 2015 to each country. In a second step, we look at the latest mortality numbers and evaluate the potential contribution of investment in water and sanitation infrastructure towards achieving MDG 4. To do so, we compare the predicted absolute mortality reduction under scenarios 1 and 2 to the observed gap between the countryspecific 2015 target and under-5 mortality as estimated in the most recent DHS surveys. The expected contribution of water and sanitation under each scenario for each country is then given by: where _CM DHS_ is under-5 child mortality in the most recent DHS survey, _CM cf_ is the predicted (counterfactual) child mortality rate, and _CM MDG_ is the country-specific child mortality rate target set in the MDGs. From the estimated absolute reductions in child mortality rates we can also derive the total number of deaths prevented (DP) per year and

> 13 Due to a lack of computational power, we were unfortunately not able to control for regional (sub-national) fixed effects in our estimation. But as we show in a previous paper (Günther & Fink, 2010) the inclusion of regional fixed effects has little impact on the point estimates obtained.

13
Figure 11: Export Decomposition <!-- Start of picture text --> 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>New exporters Stop exporters<br>Introduction of new varieties Dropping of varieties<br>Expanding existing varieties Contracting existing varieties<br>Notes:<br>Based on balanced panel<br>EIM, INEGI (Aguascalientes)<br>4.0e+07<br>2.0e+07<br>0<br>−2.0e+07<br>Value of Exports (in 1994 Pesos)<br>−4.0e+07<br><!-- End of picture text -->
**Table 11: Welfare Equations** (OLS): **East European Countries** Dependent Variable = In (household expenditure per equivalent adult)

||_Bulg_|_aria_|_Hung_|_ary_|_Pol_|_and_|
|---|---|---|---|---|---|---|
||<br>_Parameter_<br>_Estimate_|<br>_Standard_<br>_Error_|<br>_Parameter_<br>_Estimate_|<br>_Standard_<br>_Error_|_Parameter_<br>_Estimate_|_Standard_<br>_Error_|
|Intercept|8.558*|0.122|9.424*|0.042|7.556*|0.036|
|Number of children|-0.064*|0.013|-0.075*|0.005|-0.091*|0.003|
|Number of male adults|-0.065*|0.015|-0.012|0.007|-0.022*|0.005|
|Number of female adults|-0.039*|0.014|-0.011|0.007|-0.017*|0.005|
|Number of elderly|-0.084*|0.020|-0.039*|0.010|-0.025*|0.007|
|Educationofhead: primary|-0.235*|0.024|-0.228*|0.011|-0.195*|0.008|
|<br>Education ofhead:vocational/technical|0.066*|0.037|-0.109*|0.011|-0.098*|0.008|
|Educationofhead: university|0.117*|0.029|0.135*|0.014|0.207*|0.011|
|Age of head|0.021*|0.005|0.017*|0.002|0.011*|0.001|
|Age of head squared|-0.000*|0.000|-0.000*|0.000|-0.000*|0.000|
|Female head|-0.117*|0.025|-0.063*|0.009|-0.058*|0.007|
|Householdownsenterprise|0.321*|0.043|0.163*|0.015|0.229*|0.014|
|Household owns land|0.178*|0.022|-|-|0.033*|0.007|
|Household is renter|-0.326*|0.038|-0.173*|0.010|-0.047*|0.007|
|Shareofwagesinhouseholdincome|0.318*|0.036|0.172*|0.018|0.141*|0.012|
|Number of unemployed in household|-|-|-0.102*|0.009|-0.120*|0.007|
|Head is unemployed|||-|-|-|-|
|Head is inactive|-|-|-0.035*|0.017|-0.007|0.011|
|Location: non-capital city|-0.140*|0.027|0.017|0.010|-0.095*|0.011|
|Location: village|-0.246*|0.033|-0.007|0.011|-0.198*|0.013|
|Number of observations|2465||8104||16,050||
|R<sup>2 </sup>(adjusted)|0.288||0.301||0.267||
|F-statistic|63.39*||205.56*||325.47*||

_Note:_ * Indicates that estimated paraneters are significantly different from zero at the 90 percent confidence level.

57
# **Table A5. Changes in Revenue Growth Across the Business Cycle on Goods and Services: Elasticities from Fixed Effects and IV Regressions for Emerging and Advanced Economies**

|VARIABLES|(1)<br>Emerging<br>economies -<br>FE|(2)<br>Advanced<br>economies -<br>FE|(3)<br>Emerging<br>economies -<br>IV|(4)<br>Advanced<br>economies -<br>IV|
|---|---|---|---|---|
|Revenue Growth * Exp|0.37<br>[0.106]***|0.47<br>[0.108]***|1.32<br>[0.484]***|0.63<br>[0.133]***|
|Revenue Growth * Rec|-0.22<br>[0.164]|-0.18<br>[0.099]*|-0.06<br>[0.397]|-0.50<br>[0.189]***|
|Lagged Goods and Services Growth|-0.20|0.15|-0.24|0.09|
||[0.102]*|[0.086]|[0.076]***|[0.061]|
|Lagged General Government Expense Growth|0.03|0.09|-0.02|0.14|
||[0.060]|[0.074]|[0.100]|[0.086]|
|Constant|0.04<br>[0.006]***|0.01<br>[0.002]***|||
|Observations|808|432|801|418|
|R-squared|0.050|0.092|||
|Number of Countries|40|18|40|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||-0.202|0.028|
|**First Stage IV Estimates**|||**First Stage **|**First Stage **|
|Trade Weighted Growth * Exp|||1.41<br>[.2]***|1.33<br>[.08]***|
|Trade Weighted Growth * Rec|||1.77<br>[.29]***|1.36<br>[.15]***|
|**Underidentification - Weak Identification - We**|**ak Instruments**|**Tests**|||
|LM test statistic for underidentification|||44.93|29.74|
|p-value of underidentification LM statistic (Ander|son or Kleiberge|n-Paap)|0|4.95e-08|
|F statistic for weak identification (Cragg-Donald o|r Kleibergen-Paa|p)|23.97|47.36|
|Anderson-Rubin chi-sqared test of significance of<br>p-value of Anderson-Rubin chi-sqared test of endo|endogenous regr<br> genous regressor|essors<br>s|15.45<br>0.000442|28.69<br>5.89e-07|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets.*** p<0.01, ** p<0.05, * p<0.1.Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.
Table 1: Lagged shipment frequencies and shares of 2016 import statistics

|# of shipments|Share of|Share of|Share of|Share of|
|---|---|---|---|---|
|observed in 2015|shipment types|transactions|inspections|import value|
|0|0.539|0.188|0.350|0.191|
|1|0.131|0.051|0.047|0.039|
|2|0.075|0.038|0.033|0.032|
|3-5|0.108|0.084|0.068|0.071|
|6-10|0.064|0.089|0.068|0.086|
|11-100|0.079|0.374|0.291|0.392|
|_>_100|0.005|0.176|0.143|0.189|
|Totals:|671,548|3,889,775|285,611|1,597,102|

Table notes: The table shows, for frequency bins defined over 2015 imports, the share of shipment types ( _ofk_ triplets), import transactions, inspections and import value in Serbia’s 2016 imports. The final row reports the total associated with each variable. Import value is reported in millions of Serbian dinars.

calculated as the ratio of inspected shipments to shipments in year _t_ , for an _ofk_ triplet, multiplied by 100.

We begin by showing the distribution of import and inspection activity across the lagged shipment frequency bins. Table 1 reports, for 2016 Serbian imports at the HS10 level, a collection of summary statistics for each lagged shipment frequency bin. We report each bin’s share of shipment types ( _ofk_ observations), total transactions, total inspections and import value. The final row of the table reports totals for each outcome variable.<sup>22</sup> The first column shows that infrequently observed shipment types are a large majority of all shipment types observed. 54 percent of _ofk_ triplets were not observed in the prior year. Another 31 percent of shipment types were observed five or fewer times during the previous year. The sheer volume of infrequently observed shipment types poses a fundamental challenge for border inspection agencies. They have very little prior information to inform their compliance forecasts for a large majority of the _ofk_ triplets they must oversee.

> ments represent a relatively small share (of inspections, and of shipments) in our data, so the treatment of these shipments had no material effect on the results. The results we report use the broadest definition of inspections, treating documentary inspections as inspections.

> 22In appendix A we report a table of conventional summary statistics for the variables used in our subsequent regression analysis. That table contains much of the information reported here, but the data are not as useful for expositional purposes.

12
35

# Canada

The Skill Training comoonent of the NITP. From 1982 to 1985, the Canadian National Institutional Training Program (NITP) supplied funding to allow program participants to purchase basic education or skill training from the existing training Infrastructure. Discussed in Section D was the evaluation of NITP's basic education component. For the Skill Training component, Robinson and others (1985) report net Impact estimates using a treatment group/comparison group evaluation methodology. Controlling for pre-program differences In labor market variables and for Improvement in the outcome measures that occurred over time for both groups, their evidence indicates that skill training has no significant impact on earnings, while there Is some indication of a positive effect on labor force participation.

The Job Development Proaram. Introduced in 1985 as part of the Canadian Jobs Strategy, the Job Development Program Is intended to increase the employability and eamings of the long-term unemployed (workers who were jobless for at least 24 of the 30 weeks preceding their referral to the program). To achieve this objective, both wages and the direct costs of classroom training are subsidized for targeted individuals. The wage subsidy is intended to help employers cover the costs of OJT. An evaluation report by Goss, Gilroy & Associates (1989) examines net program effects for the followng Job Development Program options () the General Projects option which combines formal and on-the-job training; (ii) the Individually Subsidized Jobs option In which a sponsoring employer prepares a comprehensive proposal tailored to the specHic needs of long-term unemployed individuals who are minorities, ex-offenders, or partially disabled; and (il) the Severely Employment Disadvantaged option which provides employment In community-based projects for the long-term unemployed who are functionally lliterate, alcohol or substance abusers, ex-inmates, or are identffed as having attitude or motivation problems." Pre- and post-program labor market Information was collected from administrative data for a sample of program participants and a comparison sample of nonparticipants. Desegregating by program option and gender, Table 7 shows net program effects on employability and expected weekly