rafmacalaba/data-use-mentions-extended huggingface dataset:
Environment and Enterprise Performance Survey (BEEPS) collected in 2009 from 28 countries in Central and Eastern Europe and the Commonwealth of Independent States to examine the impact of regulation and corruption on productivity. They find that an increase in the incidence of bribes from zero to one decreases productivity by 0.045 percent, while an increase in time spent on tax administration does not have a significant impact. They also show that corruption reduces productivity more when it is more prevalent and the legal framework is weaker.

# **3. Data and methodology**

In this section, we discuss the data and the estimation methodology used in the empirical analysis. The analysis is based on the surveys from 39,732 firms conducted by the World Bank’s Enterprise Surveys in 111 economies during 2009-2017. A common sampling methodology – stratified random sampling – was followed in all the surveys along with a common questionnaire.<sup>1</sup> The sample for each country was stratified by industry, firm-size, and location. Sampling weights, which take into account the industry-size-location strata, are provided in the survey and used in all the analyses, including regressions, descriptive statistics and graphs, to ensure that the sample is representative of the non-agricultural private sector of the economy.

We start our analysis with data from 40,613 firms in 111 economies that have observations on the main variables used in the analysis. After excluding the countries with three and fewer cells and treating the sample for outliers using a program developed for data with sampling weights, the sample size drops to 39,732 firms in 111 economies. The sample size reduces slightly to 39,650 with the inclusion of the variable proxying for the investment of firms, which is a dummy variable showing whether firms bought assets in the previous year.<sup>2</sup>

> 1 Details of the sampling methodology and surveys are available at www.enterprisesurveys.org.

> 2 ES data also have an indicator on the capital-labor ratio. However, since this
Learning from Developing Country Power Market Experiences: The Case of Peru <!-- Start of picture text --> 2015 USD/MWh<br>120.00<br>100.00<br>80.00<br>60.00<br>40.00<br>20.00<br>0.00<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Marginal cost Liberalized contracts price Regulated price<br><!-- End of picture text --> Figure 5-1 Evolution of wholesale and retail electricity prices in Peru (inflation adjusted to 2015 US$/MWh).

Source: Worldbank Table 5-1 Yearly evolution of retail and wholesale prices (in 2015 USD/MWh).

|**Year**|**Average Wholesale**<br>**Pool Prices**<sup>**1**</sup><br>**(2015 US$/MWh)**|**Average Liberalized**<br>**Prices**<sup>**2**</sup><br>**(2015 US$/MWh)**|**Regulated Price**<sup>**3**</sup><br>**(2015 US$/MWh)**|
|---|---|---|---|
|**2005**|76.6|67.2|56.1|
|**2006**|80.1|63.3|52.0|
|**2007**|43.4|62.0|49.7|
|**2008**|97.1|59.1|46.6|
|**2009**|35.6|60.8|57.
_L_ 2 penalties and the mixing parameter.<sup>19</sup> The cubist model tunes over the neighborhood size used for smoothing, and the boosting iterations, using grid of all _Neighborhood × Committees_ = (2 _,_ 4 _,_ 8) _×_ (1 _,_ 25 _,_ 50 _,_ 100) combinations.

The training and out-of-sample validating are as usual on mutually exclusive draws of observations. A 4-fold validation is used to limit the computational burden of the application to manageable levels.<sup>20</sup>

> 17Food price series with zero variance do not occur in the data, but the cross-validation sampling might in theory land a draw that has extremely low price variation, particularly if the code would be applied to other countries.

> 18For instance, if a set of countries where the WFP data are complete would have been available, an interesting exercise would be to construct the _true_ food basket price and accompanying inflation. Then, in this same sample, randomly a subset of the data could be removed to simulate a pattern of missing observations and the final basket price imputation could be imputed and compared to the true data.

> 19 _L_ 1 penalizes likelihood by the absolute sum of coefficients, and _L_ 2 by the sum of squared parameter values, thereby discouraging large parameter estimates but having very different impacts when redundant parameters approach 0. In particular, _L_ 1 penalization can set parameter estimates to 0 as the penalty remains influential when a parameter approaches 0. The mixing parameter determines whether only _L_ 1, _L_ 2 or a mix of penalties, is used, balancing between the well-known Lasso Regression and Ridge regression, or mixing them (elastic net).

> 20Note that for _D_ foods, a total of _countries × M × I × D_  ̄ _country ×_ ( _folds_ + 1) _×_ Θ regressions need to be estimated, with _θ ∈_ Θ indexing the different tuning configurations and _D_<sup> ̄</
criteria)|
|St. Kitts and Nevis||65.5|||Basseterre and Charlestown|
|St. Lucia||61.7|||Castries|
|St.<br>Vincent<br>and|the|43.0|||Not available|
|Grenadines||||||
|Suriname||24.7|||Greater Paramaribo|
|Trinidad and Tobago||25.1|||selected towns|
|Uruguay||7.7|||officially defined cities|
|Venezuela, RB||12.6|2500|||
# **Firm growth and productivity in Belarus: New empirical evidence from the machine building industry**<sup>1</sup>

Jesus Crespo Cuaresma<sup>2</sup> Harald Oberhofer<sup>3</sup> Gallina A. Vincelette<sup>4</sup> JEL Classification: D22; D23; D24; P31 Sector Board: Economic Policy (EPOL) 1The authors would like to thank Martina Bakanova, Benu Bidani, Sebastian Eckardt, Martin Raiser, Pablo Saavedra, Karlis Smits, as well as participants in the Inclusive Growth and Competitiveness Course organized by the World Bank in Minsk in October 2011 for very helpful comments on earlier versions of the paper. The authors also gratefully acknowledge the funding for this research provided by the World Bank's Diagnostic Facility for Shared Growth as well as the opportunity to analyze firm level data in the machine building sector provided by the National Bank of the Republic of Belarus.

2Department of Economics, Vienna University of Economics and Business (WU); World Population Program, International Institute of Applied Systems Analysis (IIASA); Wittgenstein Centre for Demography and Global Human Capital (WIC) and Austrian Institute for Economic Research (WIFO). Address: Augasse 2-6, 1090 Vienna (Austria). Email: jcrespo@wu.ac.at.

3Department of Economics and Social Science and Salzburg Centre of European Union Studies (SCEUS), University of Salzburg. Address: Residenzplatz 9, 5010 Salzburg (Austria). Email: Harald.Oberhofer@sbg.ac.at 4The World Bank. E-mail: gvincelette@worldbank.org
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _14_ The training data contains in addition to the observed prices a small draw of 10% of the imputations generated from the previous regression estimates. These synthetic data points help balance prediction performance in thinner regions of the sample space. When the training data consists only of actual observations, the predictions may be biased toward purely observed value ranges. Adding synthetic cases using previous predictions makes the estimation problem slightly more representative of the missing data. The validation sample always consists only of actual data points, thus excluding imputations.<sup>21</sup> The model-tuning focuses on a Normalized Mean-Absolute-Error criterion. The reason for this criterion is that the MAE is less impacted by outliers than the common Root-Mean-Squared Error measure and has a stable interpretation across applications.<sup>22</sup> Note that an attempt will be made to replace outliers by missing data values and imputing them, but there is no guarantee that all outliers are captured and so an outlier-robust prediction validation metric is a safe option. There are _M_ validation results for each food item, but the interest is primarily in the robustness of the final imputed price index which combines all the predictions. The validation results are condensed for better presentation. In particular, a cross-comparable confidence score is constructed from the individual validation estimates.<sup>23</sup> The metric is defined as follows. First, a normalized MAE for food item _d_ is constructed as the ratio of the MAE of the model for that food item to the MAE obtained by a simple mean prediction. Since each MAE estimate represents an average point percent error rate due to the log nature of the price data, the individual MAE values are averaged geometrically.

where _MAE_<sup>�</sup> _m,d_ is a cross-validation estimate of MAE using the standard for-

> 21Note that throughout the simulation, the quality of synthetic training data improves. Such techniques are also being explored elsewhere.
ecosystems, which are critical to the livelihoods of many rural communities, as detailed in section 3. Soil erosion, deforestation, and water scarcity can reduce agricultural productivity and biodiversity, undermining the sustainability of local economies and food systems.

In addition, repeated weather shocks can lead to chronic poverty. Households that are frequently affected by employment and income losses may need to deplete their savings or assets, making it increasingly difficult to recover between shocks. As a consequence, households may be trapped in a state of persistent poverty. Finally, as weather shocks become more frequent and severe, households may be forced to migrate in search of better living conditions and economic opportunities. This can lead to further urbanization in Paraguay, and put additional strain on its urban infrastructure and services. Internal displacement can also lead to social tensions and deteriorate the vulnerability of both displaced and host communities.

# _Estimation challenges and limitations_

Our results are consistent with previous literature that has established association between weather shocks and poverty (Hallegatte & Rozenberg, 2017). Still, some noise in the estimates remain, which we attribute mostly to the timing of the weather shocks, the timing of data collection, and the cyclical nature of economic activities. First, EPH data is collected over a time period of three (and in some years slightly more) months, between October and December (or January), corresponding to the spring and summer months in Paraguay. As described in Section 3, extreme weather events are most common during the summer months, so there may be a variation in the effects we are able to observe depending on whether the household is interviewed in, say, the beginning of October, versus the end of December. As such, the time periods we use to construct our weather variables, which we match with survey data based on the date of the interview, cover different parts of the year, and in the case of households involved in agriculture, the agricultural calendar. We control for seasonal variation by including dummies based on the month
), immigrants from Latin America and other destinations - which mostly consist of other Western European countries and high-income OECD countries - return at a much higher rate than migrants from other regions. Returns to Eastern Europe and Africa are fairly rare, even 15-19 years after migration.

> 4In contrast with the ACS data for the United States, the exact duration of stay of foreign-born individuals is not reported in the EU LFS. Instead, the survey reports brackets of 5 years instead of a more precise duration of stay.

7
# **Table 4:  Selected rank regression results for habitat change rates**

Dependent variable: Rank of habitat change rate (Smallest = 1) Full regression dummy variable set: Salinity tolerance group, subsidence rate, IPCC scenario, GCM

|IPCC Scenario|
|---|
|A1B<br>-0.204|
|(0.07)|
|A2<br>-0.046|
|(0.01)|
|GCM|
|ECHO<br>10.926|
|(3.54)**|
|MIROC<br>12.56|
|(4.07)**|

Observations 324 R-squared 0.94 Absolute value of t statistics in parentheses ** significant at 1%

# **5.  The Potential Impact of Salinization on Poor Households**

Figure 1 and Tables 2-4 reveal a spatially-uneven pattern of salinization and fish habitat change with continued climate change, sea level rise and land subsidence in the Sundarbans region.

Data from rural areas in Bangladesh suggest that small low-value wild freshwater species are the most common fish consumed and the most important source of dietary protein for the poor (Belton et al. 2011; Thilsted 2010, 2012).<sup>12</sup> The potential impact on poor households will depend on their

> 12 The nutritional contribution of small fish species is generally high. As many small fish species are consumed whole, they provide a significant percentage of recommended intakes of calcium, vitamin A, iron, and some minerals (Thilsted 2010, 2012).

15
our analysis and other non-FDP datasets contained in the WB MDL.

Third, we also evaluate whether a given dataset contains the core set of questions recommended by EGRISS.<sup>20</sup> EGRISS recommends a set of questions that are essential for proper identification of refugees and IDPs through the International Recommendations on Refugee Statistics (IRRS)<sup>21</sup> and the International Recommendations on Internally Displaced Persons Statistic (IRIS),<sup>22</sup> respectively. For refugees, the identification questions recommended by EGRISS include (EGRISS 2018): a) Country of birth b) Country of citizenship c) Acquisition of citizenship d) Year or period of arrival in the country e) Reason for migration For IDPs, these are (EGRISS 2020): a) Place of birth b) Date of first displacement c) Date of most recent displacement d) Main reason for initial displacement e) Main reason for most recent displacement f) Place of usual residence g) Place of habitual residence We download questionnaires for each dataset from the UNHCR/WB MDLs and review each question in the questionnaires to assess whether they include any of the core questions listed above.

> 19 See Annex C: Taxonomy of  for details on the procedure taken to create a list of topic areas examined in this study. 20 EGRISS is a multi-stakeholder group that was established by the United Nations Statistical Commission (UNSC) in 2016 and now consists of members from 56 national authorities and 36 regional and international organizations.

> 21 The full document of IRRS is available at <u>https://egrisstats.org/recommendations/internationalrecommendations-on-refugee-statistics-irrs/.</u>

> 22 The full document of IRIS is available at <u>https://egrisstats.org/recommendations/international-recommendationson-idp-statistics-iris/.</u>
**Figure 10:  Coverage of social protection programs in Africa**

<!-- Start of picture text --> Share of population covered by social protection vs extreme poverty<br>South AfricaEswatini<br>Lesotho<br>Egypt, Arab Rep.GhanaBotswana Burkina Faso<br>Mauritius<br>Gabon<br>MauritaniaNamibia<br>Morocco Malawi<br>Zimbabwe<br>Côte d'Ivoire<br>Cabo Verde Kenya Rwanda<br>DjiboutiEthiopia NigerNigeria<br>Tunisia Senegal Sierra LeoneTanzaniaLiberiaAngola Congo, Dem. Rep.<br>GambiaSudanComorosGuineaCameroon UgandaChadMaliBeninTogo Congo, Rep.ZambiaMozambiqueMadagascarSouth Sudan<br>0 20 40 60 80 100<br>Share of population living in extreme poverty<br>Linear fit Share<br>100<br>80<br>60<br>40<br>20<br>Share of population covered by social protection<br>0<br><!-- End of picture text --> _Source_ : ASPIRE (Atlas of Social Protection Indicators of Resilience and Equity) database, World Bank. <u>https://www.worldbank.org/en/data/datatopics/aspire.</u> There is considerable diversity in the size and administrative design of the programs. Some are larger in size and have highly centralized management (e.g., Productive Safety Net Program in Ethiopia, National Social Safety Net Program in Nigeria, and the Child Support Grants Program in South Africa) while most programs have a narrow geographic coverage or target specific social groups. The most common delivery designs are cash transfers, followed by public work programs and school feeding programs (Beegle et al., 2018; Gentilini et al., 2020). Cash transfer programs account for 64 percent of social protection programs in Africa. The composition of programs, however, varies by income level of countries. In lowincome countries, programs are concentrated in cash transfers, public works and emergency transfers whereas
that being on staff at a wining clinic would facilitate promotions or transfers to attractive locations. We are agnostic about which element of the award, recognition or potential future gains, motivated workers; our aim is to test whether it had any discernible effects on health outcomes or users’ experiences.

> 10 All 254 clinics were told they would be audited at baseline and endline. At baseline, the audit was conducted for all clinics, and clinic staff were also reminded that it would be repeated following the endline survey. During endline we sampled clinic registers to conduct the audits from clinics in all study clinics; however, to reduce data collection costs, we only verified patient details for NFA clinics. The lack of verification in CM and control clinics cannot differentially affect reporting, since the endline data is collected under the common expectation that every clinic will be audited and prior to when endline verification occurred.

9
G. Burtch. 2020. “Examining the Heterogeneous Impact of Ride Hailing Services on Public Transit Use,” _Information Systems Research_ , 31 (3), 820–834.

- BPS, 2020. Jabodetabek Commuter Statistics (Statistik Komuter Jabodetabek: Hasil Survei Komuter Jabodetabek) 2019. Badan Pusat Statistik (BPS).

- Cairncross, J., J.D. Hall, and C. Palsson. 2021. “VancUber: The effect of ride-hailing on public transportation, congestion, and traffic fatalities,” Working Paper. <mark>University of Toronto.</mark> <u><mark>http://individual. utoronto. ca/jhall/documents/Vancouver_and_Uber. pdf</mark></u>

- Diao, M., H. Kong, and J. Zhao. 2021. “Impacts of transportation network companies on urban mobility,” _Nature Sustainability,_ 4, 494-500. 10.1038/s41893-020-00678-z.

Erhardt, G.D., R.A. Mucci, D. Cooper, B. Sana, M. Chen, and J. Castiglione. 2021. “Do Transportation Network Companies Increase or Decrease Transit Ridership? Empirical Evidence from San Francisco,” _Transportation,_ 49, 313-342.

26
Columns 1 and 2 of Table 6 show that, while, as expected, information is a determining factor of parents‟ participation, parents‟ willingness to cooperate with others in the public goods experiment remains a predictor of participation in the election of parent class representatives. This result is robust to restricting the sample to the informed parents. The estimated coefficient becomes smaller as compared to the estimates displayed in column 4 of Table 6, but it retains statistical significance at the 10% level, with a p-value of 0.057.<sup>32</sup> The smaller and less significant coefficient within the informed sample, suggests that those who invested in the group account were also more likely to select into the informed group.

# _The long route to accountability_

In this subsection, we turn our attention to the long route to accountability, i.e., citizens‟ willingness and ability to hold public officials accountable through the instrument of the vote in parliamentary elections. We asked parents whether they voted in the most recent (2009) parliamentary election and we also collected data on district-level voter turnout from official records. Here, we investigate whether parents who invested in the group account in the public goods game are more likely to have voted.<sup>33</sup> According to our School Stakeholder Survey, 93% of the parents voted in the 2009 parliamentary elections, whereas official records show that turnout was only 51%. Given this discrepancy, we investigate whether parents‟ behavior in the game correlates with turning out to vote both at the individual level using self-reported turnout and at the district level using official turnout figures. The district level analysis is reported in the next subsection of the paper.

In Table 7, we report estimated coefficients from individual-level probit regressions where the dependent variable is a dummy equal to one if the parent stated that he/she voted in the 2009 parliamentary elections and zero otherwise. We employ the specifications used when analyzing the short route to accountability, in Tables 5 and 6
516|1,498|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an OLS or probit regression. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, sex and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Drought affected status and humanitarian assistance are not included due to the lack of variation in the data within Mogadishu. Standard errors considering sampling weights in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

In terms of the effect of incidents on self-reported outcomes, the coefficient for experiencing hunger is positive but not statistically significant (column 5 in Table 3). A reduction of food consumption is likely to be associated with a larger share of household reporting to have experienced hunger. However, a non-significant result could be explained by the recall period considered in the survey instrument. The question asked to households referred to whether they had experienced hunger over the last month. As such, hunger reported by households covers between 1 and 7 days after an incident and at least 3 weeks before. We also find a deterioration of perception on police competence (column 6 in Table 3). The predicted probability of households perceiving police as being competent decreases on average by 0.34 point among exposed households in Mogadishu.

For the IV estimates, we use the same preferred specification as in the DiD approach, which includes fixed effects, characteristics of the household, household head and dwelling, as well as the drought affected status of households. The results also show a negative immediate effect on core consumption (per capita deflated) attributable to terrorist attacks from Al-Shabaab against civilians (column 1 in Table 4).

Overall, point estimates are larger with the IV approach compared to DiD estimates. Yet, the former estimates are less precise and have larger standard errors in the second stage since it only
|Ghana<br>|Rwanda*<br>|
|Burundi*|Guinea*|Senegal|
|Cabo Verde*|Guinea-Bissau*|Seychelles*|
|Cameroon<br>|Kenya|Sierra Leone*|
|Chad*|Lesotho|South Africa|
|Comoros*|Liberia*|Sudan*|
|Congo, Dem. Rep.*|Madagascar*|Tanzania|
|Congo, Rep.*|Malawi*|Togo*|
|Côte d’Ivoire|Mali*|Uganda|
|Equatorial Guinea*|Mauritania*|Zambia|
|Eritrea*|Mauritius*|Zimbabwe*|
|Eswatini*|Mozambique||

Note: The number of countries is in parentheses next to the country group name. Those in italics are low-income countries (based on the World Bank classification for FY2020). Those with * are not included in the quarterly series. The quarterly data are available in 106 countries.

61
some states below 15 percent. This contrasts with our expectations that women in cities and towns would be more likely to engage in market work, since urban areas are hubs for jobs..

**Table 1. Labor Force Participation Rates (percent) for Women and Men Aged 25–55 Urban Areas, 1983–2011**

||**1983**|**1993**|**2004**|**2007**|**2009**|**2011**|
|---|---|---|---|---|---|---|
|<sup>Women</sup>|22.7|22.7|24.6|20.6|21.2|22.2|
|<sup>Men</sup>|97.3|97.1|96.9|97.0|97.0|97.2|

Note: Data is presented for women aged 25-55 to exclude those who may be in education. This includes all women in the age range – not just wives of household heads. Source: India National Sample Survey data based usual principal status.

If we look at Indian women’s employment patterns in the context of broader demographic change and drawing upon the experience of other countries, we find that the classic facilitators of employment are in place. India’s total fertility rate (TFR) saw a 20 percent fall from 3.0 in 20012