rafmacalaba/data-use-mentions-extended huggingface dataset:
||(1)<br>Self-repor|(2)<br>Self-repor|(3)<br>Insp|(4)<br>Insp|
|---|---|---|---|---|
|Air category|0.632<sup>_∗∗∗_</sup>|0.829<sup>_∗∗∗_</sup>|-0.490<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|
||(0.068)|(0.087)|(0.062)|(0.068)|
|Firm FEs|NO|YES|NO|YES|
|Observations|16277|5624|3882|3638|
|Pseudo _R_<sup>2</sup>|0.234|0.165|0.087|0.117|

**Note:** All regressions include district, indicator and year fixed effects. Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01

Table 4: Categories water/air Self-reported data suggest that firms declare more in compliance with air than water regulations. Given that the costs of not complying with each type of regulation might be very different, these results therefore call for the attention of the regulators on the kind of regulations and penalties imposed on firms in each case.

On the other hand, the interpretation of these results regarding the inspection data needs to consider two things: first, the ability of the regulator to inspect water regulations relative to air emissions might be different and this is probably is reflected in the results
to life and security,<sup>78</sup> violence against minorities (in the case of hate crimes) affects the economic outcomes of individuals and of the country as a whole (Crehan and McCleary-Sills 2015, 6). Countries that criminalize homosexuality also have costs associated with police enforcement, judicial proceedings, and incarceration. In the particular case of transgender prisoners, for example, reports reveal that they are susceptible to abuse when they are placed within the general prison population, and avoiding the abuse increases the costs associated with the incarceration of those individuals (UNOHCHR 2012, 24). A recent World Bank study on the cost of discrimination against LGBT people in India estimates that the cost of violence against LGBT people in the country could be as high as US$30.8 billion (Badgett 2014).

Box 5: What Are Hate Crimes and Hate Speech?

Hate crimes are criminal acts motivated by bias or prejudice toward particular groups of people. To be considered a hate crime, the offense must meet two criteria: First, the act must constitute an offense under criminal law; second, the act must have been motivated by bias. Bias motivations can be broadly defined as preconceived negative opinions, stereotypical assumptions, intolerance or hatred directed to a particular group that shares a common characteristic, such as race, ethnicity, language, religion, nationality, sexual orientation, gender, or any other similar common factor.

Source: OSCE (2009) As shown in table 5, data gathered by the study show no uniformity among the pilot economies in confronting the issue of bias-motivated crimes. In general, three different approaches were found: (a) countries with specific legislation concerning hate crimes and laws concerning hate speech (Bulgaria); (b) countries with legislation regarding hate speech but no laws related to hate crimes (Mexico, Morocco, the Netherlands, and Vietnam); and (c) countries with no legislation regarding either hate crimes or hate speech (Tanzania). One good practice is
also increase the likelihood for denser interactions between exporters, no matter what the proportion of exporters in the overall cluster. Thus, not only does the specification control for the effect of other exporters, by industry and otherwise, within a district, it also includes measures of own-industry and input-output agglomeration and industrial diversity. It should be noted that clustering could also be associated with diseconomies such as congestion or increased competition. Thus, the estimations will capture the net effect of the positive and negative impacts on export participation.

> Localization economies (  _jkt_ ) can be measured by own industry employment in the district, own industry establishments in the district, or an index of concentration, which reflects disproportionately high concentration of the industry in the district in comparison to the country. I measure localization economies as the proportion of industry _k_ ’s firms in district _j_ as a share of all of all industry _k_ firms in the country for a given year _t_ . The variable takes a different value for each industry in a given district, across districts. It identifies spillovers that are associated with within-industry clustering, regardless of the final markets that these firms serve. The higher this value, the higher the expectation of intra-industry concentration benefits in the district.

There are several approaches for defining inter-industry linkages: input-output based, labor skill based and technology flow based. Although these approaches represent different aspects of industry linkages and the structure of a regional economy, the most common approach is to use the national level input-output accounts as templates for identifying strengths and weaknesses in regional buyer-supplier linkages (Feser and Bergman 2000). The strong presence or lack of nationally identified buyersupplier linkages at the local level can be a good indicator of the probability that a firm is located in that region. To evaluate the strength of buyer (supplier) linkages for each industry, a summation of regional (here district) industry firms weighted by the industry’s input (output) coefficient column (row) vector from the national inputoutput account is used: where,  _jk_
Policy Research Working Paper 9270

# **Abstract**

Hardly any literature exists on the relationship between equivalence scales and poverty dynamics for transitional countries. This paper offers a new study on the impacts of equivalence scale adjustments on poverty dynamics in the Russian Federation, using equivalence scales constructed from subjective wealth and more than 20 waves of household panel survey data from the Russia Longitudinal Monitoring Survey. The analysis suggests that the equivalence scale elasticity is sensitive to household demographic composition. The adjustments for the equivalence of scales result in lower estimates of poverty lines. The study decomposes poverty into chronic and transient components and finds that chronic poverty is positively related to the adult scale parameter. However, chronic poverty is less sensitive to the child scale factor compared with the adult scale factor. Interestingly, the direction of income mobility might change depending on the specific scale parameters that are employed. The results are robust to different measures of chronic poverty, income expectations, reference groups, functional forms, and various other specifications.

This paper is a product of the Development Data Group, Development Economics and the Office of the Chief Economist, Europe and Central Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at hdang@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
. By our calculations, this means the return to

> 4 Other indices like the World Economic Forum’s infrastructure quality index or the IMF Public Investment Efficiency Index (PIE‐ X) include survey‐based scores or distance to the frontier analysis, which means that a quality or efficiency score does not reflect the cardinal or absolute fraction of public capital operating as it should (see Section 4). The literature uses the terms quality and efficiency interchangeably as well.

> 5 This result follows from measured public capital as a share of GDP being roughly constant across countries with different levels of development (which is possibly overstated in low‐income countries with weak governance; Keefer and Knack 2007).

> 6 As in Berg et al. (2015), the level of efficiency in the LTGM‐PC has no effect on the return to new public investment because the low quality of new public investment is exactly offset by greater need for public capital due to the poor quality of past public investment. See Sections 2 and 6.5.

3
dairy, sugars, and fats in the past week in the most recent pre-displacement survey answered (eight separate variables); the number of times in the past week the household relied on less expensive foods, borrowed to purchase food, reduced the number of meals consumed, limited portion sizes of meals, and restricted consumption in the most recent predisplacement survey answered (five separate variables); an indicator equaling one if the household received food assistance in the most recent pre-displacement survey; and the number of fatalities from violent incidents and the number of violent incidents in the month before displacement. The specifications do not include the FCS nor the rCSI because they are linear combinations of the above set of variables. The specifications also include month-year indicators and district fixed effects. Each specification includes the entire sample of households used in the baseline specification- 1,116. See Appendix 5 for the coefficient estimates from each of the specifications.

> 20Prior to the escalation of the conflict, the share of households owning at least one mobile phone remained high for the rural population (81 percent), the population living below the poverty line (77 percent), and the population of all governorates (over 60 percent for each). Authors’ calculations using the 2014 Household Budget Survey.

13
(DC) method, the MBDC format combines two aspects of value elicitation format development. On one hand, the MBDC format allows each respondent to vote repeatedly on an ordered sequence of referendum thresholds, and therefore reduces the impact of the ―anchoring‖ heuristic (Tversky and Kahneman, 1974) which often presents in single or double bounded questions. The repeated referendum thresholds can provide more data to verify the coherence and credibility of the valuation results. On the other hand, in MBDC format, for each referendum threshold, a scale of ―polychotomous choice‖ response options varying from ―Definitely No‖, ―Probably Yes‖, ―Not Sure‖, ―Probably No‖ to ―Definitely Yes‖ is also provided. The polychotomous choices provide the possibility to detect and treat the potential significant uncertainties presented in each response and therefore guaranties more reliable estimations. However, the MBDC format is thought to require more cares in implementation, and confidence in successfully implementing a MBDC CV survey has not been built up, as not many MBDC studies have been conducted. This study is one of the few MBDC studies conducted so far.

This paper is organized as follows. Section 2 gives a short review on the existing valuation studies of municipal SWM project. Section 3 presents the Chinese context and the Eryuan municipal solid waste collection and treatment project. The contingent valuation survey is presented in section 4. The estimation results are reported in Section 5. Section 6 concludes the paper.

# **2. Previous Valuation Studies on Solid Waste Management**

As mentioned above, 19 CV studies have been found which had at least partially used the contingent valuation method to value the benefits of SWM projects. Most of the studies published before 2000 are for projects in the developed countries, but after 2000, more papers are found on projects in the developing world. The study subjects in the developing countries are also very different from those in the developed countries. With the relatively well established public SWM system, the CV studies conducted in the
get married within the next<br>5 years (1 if already married)<sup>_∗_</sup>|Secondary|-|-|
|Risky Sex|=1 if no condom use at last sex|-|Primary|Primary|
|Incidence of marriage|Change in ever married since baseline|-|Primary|Primary|
|Incidence of pregnancy|Change in ever pregnant since baseline|-|Primary|Primary|
|School enrollment|=1 if Enrolled in School<sup>+</sup>|-|Primary|Primary|
|Self-Efcacy|Score on Self-Efcacy Scale (10-40)|-|Primary|Primary|

Notes: _∗_ Outcome is slightly different from the one that was pre-specified; + The enrollment variable is defined as enrolled in school or enrolled when school was last in session if not currently in session, or has completed secondary schooling; _†_ the competencies score could not be measured at midline because the survey was implemented over the phone because of restrictions due to COVID-19.

32
We note that in Table 4, the value of r-squared from the regression with lasso-selected features – 0.882 – is above the one obtained from the identical exercise reported in column one of Table 3, which is 0.486. However, this includes both quarter and country fixed effects, so the comparison is not apples-to-apples. According to the last column of Table 4, the second quarter dummy explains the most considerable fraction of r-squared from the GDP growth regression. This finding is not surprising, given the dip in 2020Q2 GDP growth shown in Figure 13. Consistent with the r- squared decomposition results from the cross-section of countries – reported earlier in Table 4 – among all the real-time indicators, Google mobility and food prices are the first and second largest contributors to the r-squared from the GDP growth model, respectively. Altogether, the lassoselected real-time indicators account for 45.7% of the r-squared obtained after fitting the model reported in column three of Table 4. In other words, the features in question explain 0.457 × 0.882 = 40.3% of the variation in growth rates in the panel of high-income countries. It might be useful to contrast this figure with that obtained from the cross-section of countries that includes states with lower income.

According to column one of Table 3, lasso-selected features contribute to (1 −0.0574) = 94.3% of the overall r-squared reported in column one of Table 2. The r-squared in question is equal to 0.486. Therefore, the lasso-selected features explain 0.486 × (1 −0.0574) = 45.8% of the variation in GDP growth in the cross-section of 61 countries. This explanatory power of real-time indicators is comparable to that observed in the sample of high-income countries. From this observation, we conclude that, among all the real-time indicators considered in our analysis, Google mobility data and staple food prices are the most robust predictors of GDP growth and that their explanatory power is highly comparable across the
_, . . . , M_ , where _E_ [ _ηc_<sup>_∗_]=0</sup> <u>1</u> _M_ and _E_ [ _e_<sup>_∗_</sup> _ch_<sup>]=0.</sup> Thus, the ELL estimator _M_ � _m_ =1<sup>_y_ ̄</sup> _c_<sup>_∗_(</sup><sup>_m_)</sup> , which approximates _E_ ( ̄ _yc_ ), reduces to the regression synthetic estimator, _X_<sup> ̄</sup> _c_<sup>_′β_(MR,2010).Ontheotherhand,underMR(2010),</sup> conditioning on the survey sample ensures the estimator includes the random location effect, since _E_ [ ̄ _yc|ηc_ ] ≈ _X_<sup> ̄</sup> _c_<sup>_′β_+</sup><sup>_ηc_.Mechanically,however,conditioningonsurveydatarequiresthelinkingof</sup> areas across surveys and census, something that is not always straightforward.<sup>2</sup> Other differences between ELL (2003) and MR (2010) are the computational algorithms used to obtain point and noise estimates; see Corral, Molina and Nguyen (2020 - CMN henceforth) for further discussion.

ELL’s (2003) approach to obtain estimates builds upon the multiple imputation (MI) literature in that it uses a single algorithm that produces point and noise estimates by varying model parameters across simulations (see Tarozzi and Deaton, 2009 as well as CMN, 2020). The use of MI methods for obtaining point and noise estimates has shortcomings, however. Under multiple imputation, the method that yields the lowest MSE does not necessarily yield valid statistical inference (Van Buuren, 2018), which is contradictory to the goal of small area estimation in improving precision. Accordingly, CMN (2020) show using
# **<u>Table A1: Variable Descriptions</u>**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Log of sales|Log of real annual sales in USD|World Bank Enterprise Surveys|
|Log of labor|Log of total number of full time<br>employees, adjusted for temporary workers|World Bank Enterprise Surveys|
|Log of capital (replacement value)|Log of cost for establishment to re-<br>purchase all of its machinery|World Bank Enterprise Surveys|
|Log of raw materials (costs)|Log of cost of raw materials and<br>intermediate goods used in production|World Bank Enterprise Surveys|
|Number of water shortages per day in a<br>typical month|Number of incidents of water insufficiency<br>per day in a typical month. Water<br>insufficiency is defined as a shortage that<br>affects production. Outages that do not<br>affect production are not counted|World Bank Enterprise Surveys|
|Average duration of water shortage|Average duration of insufficient water<br>supply in hours|World Bank Enterprise Surveys|
|Number of electrical outages per day in a<br>typical month|Number of power outages per day in a<br>typical month. Only outages that affect<br>production are counted|World Bank Enterprise Surveys|
|Average duration of electrical outages<br>(hours)|Average duration of electrical outages in<br>hours|World Bank Enterprise Surveys|
|Losses due to breakage/spoilage shipped<br>domestically (% of value of the products)|Percentage of value of products the<br>establishment shipped to supply domestic<br>markets lost while in transit because of<br>breakage or<br>spoilage over the last fiscal year|World Bank Enterprise Surveys|
investment experience and prospects for new investments?

This paper is organized as follows. After reviewing the existing literature in Section 2, in Section 3 we introduce the aforementioned framework, sample and survey methodology, and Section 4 presents the results. Section 6 then ‘reality-checks’ the existing, data based assessments of countries’ readiness for investment by comparing it to the results from the investor survey. Section 6 provides a summary and lessons for policy.

> 4 A further challenge is the definition of what constitutes a ‘domestic’ investor. For our purposes, we define domestic investors as those investors that are incorporated on the African continent.

3
a transition from one income group to another—the share of individuals found in the diagonal is lower than for disposable income.

The net effect of direct transfers and taxes is to push more individuals down to a lower income group than are raised to a higher income group. Direct interventions push more women (3 percent) than men (2 percent) down to a lower income group, and many more women then fall below the absolute poverty line (birr 5,050).

Overall, fiscal policy moves about one in five individuals (20.8 percent for men, 18.4 percent of women) from one income group to another, and more women than men transition to a higher income group, making them relatively better-off. Moreover, the income mobility matrices show that the intervention resulted in more transitions to both lower (downward mobility) and higher (upward mobility) income groups among poor or near-poor women and men; among women, about 60 percent of those for whom there was a transition had market income less than the 40<sup>th</sup> percentile.

# _Effect on Poverty and Inequality_

The distributional impacts of fiscal policy are assessed using changes in poverty levels and income inequality. This analysis uses two poverty lines: (1) a relative line set at two-thirds of the median market income; and (2) an absolute line obtained by calibrating a threshold that could produce the official national poverty rate. Table 4 presents results of fiscal policy effects on poverty incidence, gap, and severity by gender at different CEQ income concepts. The poverty indices for both absolute and relative poverty lines tend to be higher for women at all income levels. Moving from market income to net market income shows the change in poverty due to direct taxes, which increased absolute as well as relative poverty for both men and women.

**Table 4: Poverty** **<u>by CEQ Income Concepts and Gender</u>**

||**Mark**
of college educated workers and the college premium, respectively. The former rises over time as young, more educated cohorts replace older, less educated ones. The replacement is complete around the year 2045, when the economy reaches steady state.

**Figure 16: Free College and and the College Premium**

<!-- Start of picture text --> 45 3<br>40 2.5<br>35 2<br>30 1.5 ω = 3.2<br>ω = 2.5<br>25 1 ω = 1.4<br>20 .5 ω = 1.2<br>2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050<br>Year Year<br>a. Share College in Labor Force b.College Premium<br>Percent<br>College premium<br><!-- End of picture text --> _Source_ : Own calculations based on estimates.

_Notes_ : Panel a shows predicted share of college graduates in the labor force, calculated as the share of workers (of all ages) with a college degree relative to all workers (all ages and educational attainment) in the economy. Panel b shows predicted college premium, calculated as the ratio between wage for college graduates and wage for high school graduates.

> 29There is a large number of estimates for this parameter in the literature. For example, Katz and Murphy (1992) estimate it to be 1.4 for the U.S. for the period 1963-1987, but the low range of estimates also include 0.5. Card and Lemieux (2001) estimate it for the United States (1959 to 1996) and the United Kingdom (1974-1996) and find estimates between 2 and 2.5. Following Card and Lemieux (2001), we estimate the production function parameters using Colombia’s household surveys (SEDLAC) for 2001-12. Due to data limitations, college graduates include
0.018<br>-0.030<br>-0.032<br>-0.040<br>-0.037<br>Official line 2009 Official line 2017<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017, 2009), INE and Fiscal Accounts and CEQ Methodology.

# **V. Conclusions**

This paper attempted to update the impact analysis of fiscal policies in Uruguay for 2017, as well as compare those results with previous studies. Uruguay shows a strong social compact, at least 25
0.00|0.00|0.00|
|_Prob. of Fall behind_|||||||||
|Tier 1 to Tiers 3 & 4|11.01|5.41|2.54|1.66|4.30|1.20|0.30|0.00|
|Tier 2 to Tiers 3 & 4|36.8|28.77|17.70|18.40|26.00|25.60|22.80|24.40|

_Source_ : SPADIES for observed data; model simulations for predicted data. _Notes_ : Values are expressed in percentages (%).

# **7.5 The Role of**

Since our estimates show that class completion is much more responsive to effort than ability, we now examine the role of

## **7.5.1 Average effort over time.**

We begin by looking at average student effort by year (Figure 11). On average, effort is relatively high in years 1 and 4. In year 1, students work hard to complete classes and mitigate dropout 26