rafmacalaba/datause-encoder-data huggingface dataset:
detailed analysis of its current management practices and performance and, crucially, recommendations for change.

The second phase was a four-month _implementation_ phase given only to the treatment experimental plants. In this phase, the consulting firm followed up on the diagnostic report to help introduce as many of the 38 management practices as the plants could be persuaded to adopt. The consultant assigned to each plant worked with the plant management to put the procedures into place, fine-tune them, and stabilize them so that employees could readily carry them out.

The third phase was a _measurement_ phase, which lasted until November 2011. This involved collection of performance and management data from all treatment and control plants. In return for this continuing data, the consultants provided light consulting advice to the treatment and control plants (primarily to keep them involved).

# **_II.B. The Initial Experimental Results – Management Practices_**

The intervention led to increases in the adoption of the 38 management practices in the treatment plants by an average of 38 percentage points by August 2010 (approximately one year after the start of the intervention). This adoption rate dropped by 3 percentage points in the second year of tracking, showing persistence in practices after the consultants had exited the firms. Not all practices were adopted equally, with firms adopting the practices that (unsurprisingly) were the easiest to implement and/or had the largest perceived short-run payoffs, e.g. the daily quality, inventory and efficiency review meetings. This adoption also occurred gradually, in large part reflecting the time taken for the consulting firm to gain the confidence of the firms' directors. Initially many directors were skeptical about the suggested management changes, and the intervention often started by piloting the easiest changes around quality and inventory in one part of the factory. Once these started to generate improvements, these changes were rolled out and the firms then began introducing the more complex improvements around operations and human resources.

In contrast, the control plants, which were given only the one-month diagnostic and corresponding recommendations, increased their adoption of the management practices, but by only 12 percentage points on average. This is substantially less than the increase in adoption in the treatment firms, indicating that the four months of the implementation phase were important 6
# **Is International Trade Always Beneficial to Labor Markets? A Case Study from Egypt**

Raymond Robertson (Texas A&M University)<sup>1</sup> Mexico Vergara (World Bank) Gladys Lopez-Acevedo (World Bank) JEL: F1, J2, C1 Keyworks: Imports, Trade, Labor Market, Informality, Econometrics, Bartik, Egypt

> 1 The authors gratefully acknowledge the financial and analytical support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs.
# CONTENTS

|ACKN|OWLEWDEMENT<br>..............................................|i|
|---|---|---|
|1.|INTRODUCTION .1||
|II.|ENTERPRISE FINANCE AND T'HE STRUCTURE OF THE GOVERNMENT BUDGET.|2|
|III.|MAKING SENSE OF POLISH PROFITS<br>_.5_||
|IV.|MOVEMENTS IN PROFrTS1990-91 .9||
|V.|ANOTEIN PASSING: INVENTORY ACCUMULATION IN THE NATIONAL AccouNTs**.13**||
|VI.|PROFITS, TAX REVENUE AND WAGE BEHAVIOR .14||
|VII.|FoRECASTING TAX REVENUES: WHAT WENT WRONG?......................|21|
|VIII.|THE CURRENT FISCAL CRISIS: POLICY OPTIONS FOR INCREASING TAX REVENUES.22||
||APPENDICES||
||APPENDIX 1: HISTORICAL COST PROFIT, CASH FLOW, AND INVENTORIES.26||
||APPENDIX 2: INVENTORIES AND THE INFLATION BIAS .31||
||REFERENCES .36||
> <sup>13</sup>

margin, politicians can influence which teachers are hired or where they are posted, but most teachers and postings are fixed from year to year. Public investment spending, on the other hand, disproportionately consists of new construction that benefits particular constituencies. Given the characteristics of public investment, politicians interested in providing narrowly targeted services to constituents should therefore be disproportionately interested in increasing public investment. In fact, as the phrase “pork barrel spending” recalls, political preferences for targetable public investment spending are well-known. Public investment spending as a fraction of GDP is therefore used here as a measure of government incentives to target public spending to specific constituencies.

# **Explaining differences between young and old democracies**

The key concern here is to assess whether the age of democracy explains variation in policy choices across democracies. Numerous alternative hypotheses regarding policy variation must also be taken into account, however. This section describes the age of democracy variable and the other controls that enter into the econometric specifications analyzed below.

# **_Measuring the age of democracy_**

The puzzle explored in this paper is why the institution of competitive elections, a defining characteristic of democracies, yields sharply different outcomes across countries. In constructing a measure of the age of democracy, therefore, the core democracy variable is based on the seven-point Legislative and Executive Indices of Electoral Competition ( _LIEC_ and _EIEC_ ) from the _Database on Political Institutions_ (Beck, et al. 2002). These variables are objective and narrow, excluding, unlike other measures of democracy, either other institutional features of countries (the presence of checks and balances) or outcomes
**Table 3: Regression Results for Step 2: Spill-over of Domestic Food Price Inflation**

**to other Domestic Prices Inflation**

**And Root Mean Square Error of the Forecasted Domestic Price Inflation***

||**Using Output gap**|**Using Output Growth**<br>**gap**|
|---|---|---|
|**Using FAO food index**|||
|_Coefficient_|_0.4841_|_0.5918_|
|_R2 adjusted_|_0.7210_|_0.5868_|
|<br>_RMSE_|_46.4037_|_31.4460_|
|**Core**<br>**Using FAO Cereals sub-**<br>**index**|||
|_Coefficient_|_0.8782_|_1.0408_|
|_R2 adjusted_|_0.6168_|_0.6617_|
|_RMSE_|_40.2721_|_25.7254_|
|**Using FAO food index**|||
|_Coefficient_|_0.65497_|_0.1458_|
|_R2 adjusted_|_0.7161_|_0.6968_|
|<br>_RMSE_|_37.4357_|_33.6075_|
|**Non-Food**<br>**Using FAO Cereals sub-**<br>**index**|||
|_Coefficient_|_0.8463_|_0.5883_|
|_R2 adjusted_|_0.7555_|_0.7374_|
|_RMSE_|_32.0512_|_24.7098_|

**_Note_** : *All figures are calculated from coefficients estimated at 5% significance level at least, and RMSE is calculated for the period 2010M7- 2011M6.

Finally, there is evidence that international prices have asymmetric effect on domestic prices (Table 4). Following the approach described in section 4, domestic food price inflation is found to increase by only 5.4% when inflation of FAO food index increases by 10% and by 1.8% when inflation of FAO food index increases by the same magnitude. Using the FAO cereals sub index inflation, the asymmetric effect is less pronounced: an increase of 10% in international food prices would cause a 2.3% increase of domestic food prices, while a decrease of the same magnitude would cause a decline of 1.8%.

**Table 4: Coefficients of International Price Inflation in Step 1 of the 2-step Regression**

||**FAO food index**|**FAO Cereals sub-**<br>**index**|
|---|---|---|
|**Slope of inflation increases**|0,5366|0,2317|
|**Differential slope**|-0,3548|-0,4086|
|**Slope of inflation decreases**|0,1817|-0,1769|

Note: * All coefficients are significant at the 95% level.

15
# Effects of the Internet on Participation: Study of a Public Policy Referendum in Brazil

Paolo Spada, University of British Columbia* Jonathan Mellon, Oxford University Tiago Peixoto, World Bank Fredrik M Sjoberg, Data-Pop Alliance February 2015 Words: 7,368 Abstract Does online voting mobilize citizens who otherwise would not participate? During the annual participatory budgeting vote in the southern state of Rio Grande do Sul in Brazil – the world’s largest – Internet voters were asked whether they would have participated had there not been an online voting option (i-voting). The study documents an 8.2 percent increase in total turnout with the introduction of i-voting. In support of the mobilization hypothesis, unique survey data show that i-voting is mainly used by new participants rather than just for convenience by those who were already mobilized. The study also finds that age, gender, income, education, and social media usage are significant predictors of being online-only voters. Technology appears more likely to engage people who are younger, male, of higher income and educational attainment, and more frequent social media users.

Classification (JEL): P16, D72, H41, and H7. Keywords: Participation, Voting, Participatory Budgeting, Internet, and Digital Divide.

*Note that author order is determined using bounded randomization. All authors contributed in equal shares.

Acknowledgements: we would like to thank Hollie Russon Gilman and Quinton Mayne for help with the survey instrument and implementation. Thank you to David Schmidt, Paulo Coelho, and Ricardo Almeida at Rio Grande do Sul’s Secretariat of Planning for facilitating this work. We would also like to thank Ricardo Fritsch, Carlson Aquistapasse, and Guilherme Donato at PROCERGS for providing the data on the PB vote. We are also thankful for comments from Deborah Wetzel (World Bank), Graham Smith (University of Westminster), Hélène Landemore (Princeton), Jonathan Fox (American University) and Roland Clarke (World Bank). Finally, thanks to Amy Chamberlain for editorial assistance. This work has been funded by the Governance Partnership Facility and led by the World Bank’s Digital Engagement Evaluation Team (DEET).
regulatory environment related to SME lending without compromising prudence<sup>68</sup> . The feasibility of tapping capital markets via innovative SME loan securitizations (e.g. by using credit enhancements in the same way that SHF has done for mortgage securitizations) should also be assessed in light of the on-going growth in institutional investor assets. Constraining factors that currently inhibit such securitizations (e.g. lack of product standardization, insufficient credit history, inconsistency in loan origination procedures across banks, inadequate size or diversification of the loan pool etc.) would need to be addressed appropriately in light of similar experiences by other countries<sup>69</sup> .

> 68 A more speculative idea would be the pooling of industry-wide SME credit data by a CNBV-managed consortium in which all credit institutions would be obliged to provide – on a confidential basis – minimum standardized information that could be used to calculate generic SME credit scoring models by size, sector and location; these could then be utilized (and improved upon based on other proprietary information) by all market participants.

> 69 Examples include the USA’s _Small Business Administration_ program, Spain’s _Fondo de Titularización de Activos Pyme_ , and Germany’s KfW-managed _Programme for Mittelstand Loan Securitization_ .

42
Examining the time series data of SR-VIS brightness values reveals both how villages vary in their average brightness level, as well as the variability of the brightness values over time. Figure 3 plots the brightness of the village of Sheorajpur in Allahabad district on every night from 1993 to 2013. Electrification of Sheorajpur as part of India’s national electrification program, that is Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY) program, was completed on 30 June 2007. Light output in the village did not increase much until about 2010, suggesting a lengthy period before electrical power was widely available and in use by residents of the village. The bars indicate one standard deviation above and below the mean of all statistically recalibrated visible band brightness observations in each year. The increase in the size of the bars in recent years shows that variability in light output has increased. Power outages are an obvious reason why a village would appear brightly lit on one night but far dimmer on another. We use this information on the variability of light output over a village to construct a new Power Supply Irregularity (PSI) index.

We define PSI as the unexplained variability in light output relative to the predicted variability of light output for a village, calculated using data over a time period of interest. The excess variability is calculated as the residual of a regression using data from all villages that predicts the standard deviation of light output given the brightness of each village. To fit the curvilinear relationship between standard deviation and brightness in the DMSP data, we estimate a regression model including several polynomial terms. More details can be found in the Appendix.

10
**Table 2: Parameter estimates for aggregate energy**

||**Base**|**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|
|---|---|---|---|---|---|---|
|**_Parameter estim_**|**_ates_**||||||
||3.47***|2.84***|3.83***|3.96***|2.84***|3.50***|
|y�|(0.23)|(0.22)|(0.18)|(0.27)|(0.20)|(0.21)|
|�|‐0.15***|‐0.12***|‐0.17***|‐0.16***|‐0.09***|‐0.14***|
|y�|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
||‐0.17***|‐0.16***|‐0.15***|‐0.10***|‐0.12***||
|p�|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|―|
|_Inv. share_|―|0.19***<br>(0.04)|―|―|―|―|
|_Urbanization_|―|―|0.32***<br>(0.01)|―|―|―|
|_Pop. density_|―|―|―|‐0.28***<br>(0.07)|―|―|
|_Trend_|―|―|―|―|‐0.01***<br>(0.00)|―|
|_Nom. price_|―|―|―|―|―|‐0.15***<br>(0.01)|
|_RER_|―|―|―|―|―|‐0.06***<br>(0.01)|
||‐0.08***|‐0.09***|‐0.11***|‐0.12***|‐0.10***|‐0.09***|
|ρ|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|**_Key statistics_**|||||||
|Hausman test|13.44|20.10|14.76|11.45|20.04|43.83|
|_p_‐value|0.00|0.00|0.01|0.02|0.00|0.00|
|Log‐likelihood|6,086|6,043|6,087|5,798|6,105|6,093|
|Observations|3,235|3,170|3,183|2,940|3,235|3,235|
|Countries|63|63|62|63|63|63|

**Notes** : The dependent variable is the logarithm of aggregate energy consumption. Three (***), two (**), and one (*) asterisks denote significance of parameter estimates at 1, 5, and 10 percent level, respectively. Stand‐ ard errors in parentheses. “―” indicates that the corresponding variable was not included in the model.

_— 19 —_
additional SHG members registered at the time of the baseline but not included in the baseline survey. See Figure 2 for a Timeline of Events.

Table 1b compares the baseline characteristics across treatment and control groups. The Appendix details how the variables used were created. All members used in the analysis were registered members at the time of the baseline. Table 1c reports the number of observations per variables at baseline and at follow-up. Some variables have at most 726 non-missing observations if the information was only elicited in April 2004 or 1,117 if we also asked the question retrospectively at follow-up for the additional sample of 391 members that were included in the follow-up but were not in the baseline. We reached 86% of the baseline individuals in the follow-up survey. Appendix Table 1 compares the baseline characteristics of those reached in the follow-up to those not reached.

About half of the household income of these farmers came from farm activities, while the rest came from employment (both formal and informal), remittances, or pensions and gifts. Most farmers own the land they cultivate, and the median farm size was one acre. Farmers grew subsistence crops (beans, maize, potatoes, and kale) half of the time and cash crops such as coffee, bananas, or tomatoes 34 percent of the time. Only twelve percent of the farmers were already growing French beans, and nobody baby corn, the main horticulture crops promoted by DrumNet.

Farm operations are typically done using only manual human labor, with fewer than five percent utilizing animal labor or machinery to boost productivity. This is not surprising given the small size of the farms. In addition, three quarters of those surveyed rely solely on family labor, not requiring hired labor to plant or harvest crops.

10
**Table 4. Marginal probability of study documenting above-average exposure and adverse impact for the poor**

|||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|---|
|||Poor mor|e exposed<br>||Poor more adv<br>|ersely affected|<br>|
|||All|Excl.<br>CCDRs|All|Excl.<br>CCDRs|All|Excl.<br>CCDRs|
|Type of shock|Climate change|||0.216***|0.181***|1.127***|0.976∗∗∗|
|||||(0.030)|(0.029)|(0.199)|(0.057)|
||Heat|0.224***|0.156***|||||
|||(0.055)|(0.041)|||||
||Flood|0.190***|0.367***|0.280***|0.290***|1.224***|1.345∗∗∗|
|||(0.067)|(0.003)|(0.058)|(0.050)|(0.111)|(0.114)|
||Drought|0.289***|0.536***|0.189***|0.179***|0.896***|0.774∗∗∗|
|||(0.040)|(0.001)|(0.044)|(0.038)|(0.064)|(0.210)|
|Country or region||-0.265**|-0.339***|-0.240***|-0.410***|-1.558***|-2.005∗∗∗|
||United States|(0.134)|(0.065)|(0.049)|(0.072)|(0.273)|(0.240)|
||Sub-Saharan|0.068***|-0.079***|-0.143***|-0.344***|-0.871***|-1.519∗∗∗|
||Africa|(0.009)|(0.026)|(0.017)|(0.067)|(0.124)|(0.273)|
||South Asia|||0.079***|-0.012**|0.154|-0.250∗|
|||||(0.009)|(0.005)|(0.107)|(0.105)|
||East Asia and<br>|-0.235***|-1.042***|0.077***|0.004***|0.280***|-0.0969|
||Pacific|(0.017)|(0.097)|(0.007)|(0.001)|(0.002)|(0.069)|
||Latin America|-0.423***|-0.762***|0.309***|0.246***|1.187***|0.964∗∗∗|
||and Caribbean|(0.014)|(0.056)|(0.039)|(0.032)|(0.258)|(0.135)|
||Europe and|-0.239***||||||
||<br>Central Asia|(0.049)||||||
||Middle East and|0.107***|-0.365***|||||
||North Africa|(0.041)|(0.049)|||||
|Outcome Channels|Income<br>reduction|||||0.570***|0.632∗∗∗|
|||||||(0.068)|(0.028)|
||Increased<br>poverty|||||0.849***|0.657∗∗∗|
|||||||(0 .062)|(0.113)|
||Asset losses|||||-0.100|-0.599∗∗∗|
|||||||(0.184)|(0.084)|
||Human capital<br>losses|||||0.177***|0.264∗∗∗|
|||||||(0.001)|(0.025)|
|Number of observatio|ns|148|125|424|358|424|358|
|Mean of the depende|nt variable|<br>0.68|0.58|0.80|0.76|0.80|0.76|

_Note_ : _CCDR = Country Climate and Development Report._ Marginal probabilities from a probit regression of the probability that a study finds a greater exposure of the poor to climate shocks (columns 1–2) or a greater impact of climate shocks on the poor (columns 3–6) than for other households. Columns 5–6 include the outcomes of studies that analyze the impact of climate shocks on the poor. Sample has 70 studies, of which 37 are CCDRs. Even columns exclude the CCDRs. The unit of observation in the regression is the study estimate. Excluded shock category is any natural disaster, excluded region or country is global. Level of analysis dummies: subnational, household, individual, and country is the excluded category. Standard errors clustered at the study level. Significance: * p<0.10, ** p<0.05, *** p<0.01.

18
Policy Research Working Paper 7349

# **Abstract**

With data from the nearly 6,000 households in the Nepal Living Standards Survey of 2010–11, this paper finds that the mean reduction in household firewood collection associated with use of a biogas plant for cooking is about 1,100 kilograms per year from a mean of about 2,400 kilograms per year. This estimate is derived by comparing only households with and without biogas in the same village, thus effectively removing the influence of many potential confounders. Further controls for important determinants of firewood collection, such as household size, per capita consumption expenditure, cattle ownership, and unemployment are used to identify the effect of biogas adoption on firewood collection. Bounds on omitted variable bias are derived with the proportional selection assumption. The central estimate is much smaller than those in the previous literature, but is still large enough for the cost of adopting biogas to be significantly reduced via carbon offsets at a modest carbon price of $10 per ton of CO2e when using central estimates of emission factors and global warming potentials of pollutants taken from the scientific literature.

This paper is a product of the Environment and Energy 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 lead author may be contacted at som@isid.ac.in.

_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
Assuming _uA_ and _uS − uL_ remain unchanged as benefits increase, From the above equation, we see that due to the complementarity of _A_ and _L_ for the large retailer, the response of the marginal revenue of _A_ responds to changes in the valuation of _S_ and hence _L_ as well given they both rise together. This term,<sup>_<u>du</u>_</sup> _db_<sup>_<u>L</u>_,isthenmultiplied</sup> by the pass-through rate, which is composed of a combination of marginal revenues and _∂pA ∂pA_ inverse demand slopes 1 _− ∂MRA∂q−A_<sup>_∂MRL_</sup> _− ∂MRA∂q−L_<sup>_∂MRL_</sup> , and is equal to _h_<sup>_′_</sup> ( _l_<sup>_−_1</sup> )<sup>_′_</sup> , consistent _∂qA ∂qL ∂qA ∂qL_ with equation (52).

Regarding other models, Zhou (2014) finds that multi-product search effect can generate both loss leading and cross-category pricing in a multi-product sequential-search model. In his model, a rise in valuation for one good can lead to an increase in price for the other. Other models include Rhodes (2015), who highlights an alternative mechanism in which a firm’s optimal advertising strategy can give rise to loss leading. Johnson (2017) points to a different mechanism that leads to loss leading in which asymmetric multi-product retailers compete for consumers who make unplanned purchases. These models have different predictions on whether prices will rise together when one product category is shocked, and we leave a more detailed analysis of the implications of these models to future work.

71