rafmacalaba/data-use-mentions-extended huggingface dataset:
IBRD 47747 | MARCH 2024

<!-- Start of picture text --> Broad Norm: men and women should have equal opportunities<br>Misperception ofthe norm: |Perceived Community Norms - Personal Beliefs (%)|<br><!-- End of picture text -->

<!-- Start of picture text --> Z<br>-<br>; a<br>ng \ vn :<br>F * tee ns<br>Ss eG, \<br>‘@ — a<br>Sin, a 7 ! ; i Ly<br>A | } , /<br>45-50 - yy Ni@eis<br>Mi 35-40 neae. sy “4<br>WM 30-35 4<br>WmMm 25-3020-25 Sy ae<br>my 15-20<br>my 10-15<br>fy 5-10<br>0-5<br>No data<br>-<br><!-- End of picture text -->

Misperception is determined by the disparity between the Community perceived norm (on a 0-100 scale) and the Aggregate Level ofAgreement with the norm within the country (on a 0-100 scale). Here we present the absolute values ofthe misperception as all countries have a negative value suggesting a general misperception of the support for gender equality globally. Darker shades ofthe map represent a greater misperception of the norm i.e. a greater underestimation in the level of support for gender equality in opportunities in the country. Source: Gender Equality at Home Data, 2020.
21 jurisdictions and has put them at greater finanoial risk to finanoial problems at the central government level.

The Ecuador data also show extremely large increases In the reliance upon grants. In the Ecuador case, however, the growth occurred primarily during the 1970s with the proportions leveling off since then. While not necessarily tied to macro economic conditions, it Is interesting to note that both Mexico and Ecuador benefited from the petroleum boom during the late 1970s. The fiscal condition of the granting government is certain to have some impact on its willingness to transfer revenues to subsidiary jurisdictions.

Nigeria, another oil-producing nation, similarly increased the reliance of states on revenue transfers in the form of shared taxes. The role of grants, however, declinad during the late 1970s. As In the case of Mexico, the changes observed were primarily due to legislative changes introduced during the latter half of the 1970s. As of 1975 revenue sharlng on the basis of derivation of tax revenues was diminished in importance; in return, the national government increased contributions to the Distributable Pool Account (DPA)..<sup>3</sup> Subsequently, a new 1979 constitution 12See The World Bank, "Mexico: Financing State and Municipalities, Trends, Issues and Recommendations," Mexico Programs Division, Urban Projects Division, Latin America and the Caribbean Regional Office (mimeo), 1987. 1 3Wilfred A. Ndongko, "Revenue Allocation and the Stability of the Nigerian Federation," Public Administration and Finance (April-June 1981), pp. 151-164.
** .  The reach of Romanian SOEs goes beyond the typical network sectors and encompasses activities without a clear economic rationale for their presence, such as manufacturing, accommodation, and food service activities. Like in many countries, Romanian SOEs do not always operate under the same rules and conditions as privately-owned enterprises (POEs) (World Bank 2020; Iootty, Pop and Pena, 2021; OECD 2023). The advantages and privileges they enjoy can create an unequal footing with POEs, thus distorting key market outcomes and limiting the efficiency  of resource allocation across firms and sectors.

**Romanian SOEs tend to perform worse than their private peers** . They are less profitable and generate lower revenues per employee than their private peers. Besides, they were found to have high wage premia, which weighs on their financial bottom line (IMF 2019). The prevalence of SOEs in the Romanian economy, their inefficiencies,  notably in key sectors like energy and transport that provide essential inputs to other sectors, coupled with their lower profitability and higher wage premium, have been identified as imposing a significant burden on public finances and economic growth (EC 2015; World Bank 2018). Historically, SOEs had higher debt levels, including through overdue payments to suppliers (Marrez 2015). Weak governance of SOEs and state aid support to dwindling industries with significant SOE presence (e.g., railways), among others, have induced resource misallocation towards less productive firms, such as SOEs, thus dampening aggregate productivity gains (World Bank 2018).

**This papers sheds additional light on the performance of Romanian SOEs in the recent years (including the early COVID-19 period) and their effects on markets** . The paper addresses several questions relating to SOE presence in markets. For that the paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes (hereafter referred to as MoF firm-level data) for the period 2011-2020, combined with the new World Bank Businesses of the State (BOS) database that
017)|(0.031)|
|Export Orientation Index||1.413***||1.292***|0.904***|0.941***|0.825***|0.867***|0.935***|0.713***|
|||(0.055)||(0.051)|(0.040)|(0.047)|(0.084)|(0.042)|(0.047)|(0.089)|
|Log of agricultural input costs<sup>a</sup>|||||0.482***|0.482***|0.491***|0.519***|0.512***|0.544***|
||||||(0.012)|(0.014)|(0.023)|(0.012)|(0.014)|(0.024)|
|Simpson Index of Diversification|||||0.591***|0.603***|0.550***|0.630***|0.619***|0.668***|
||||||(0.048)|(0.051)|(0.140)|(0.050)|(0.053)|(0.143)|
|Number of unpaid ag family<br>|||||0.046**|0.035*|0.113*|0.045**|0.031|0.154**|
|workers|||||(0.018)|(0.019
Figure 3: The Treatment Effect for Migrants Declines with the Relative Health Insurance Premium <!-- Start of picture text --> 0 .02 .04 .06 .08 .1<br>Effect 95% CI<br>.25<br>.2<br>.15<br>.1<br>.05<br>0<br>-.05<br>-.1<br>-.15<br><!-- End of picture text -->

- (a) The effect on all migrants, predicted from Model 3 of Eq. (3)

<!-- Start of picture text --> 0 .02 .04 .06 .08 .1<br>Effect 95% CI<br>.25<br>.2<br>.15<br>.1<br>.05<br>0<br>-.05<br>-.1<br>-.15<br><!-- End of picture text --> (b) The effect on migrants without a contract, predicted from Model 3 of Eq. (4) **Source** : 2015 and 2016 waves of the Rural-Urban Migration in China (RUMiC) Survey, and RUMiC Information Intervention Survey, 2016. **Notes** : The dot, dash, long dash lines indicates the 10th percentile, 25th percentile and median relative premiums.

39
distance in time across different treatment periods across countries. The latter term is a new addition compared to estimators used previously in the literature. The two parameters allow weighting the difference between treated and control units (from the TWFE component) by how much the two are comparable.<sup>14</sup> We use the SDID estimator to identify the Average Treatment effect on the Treated (hereafter, the ATT) of signing a PTA containing at least one NTP addressing one of the three policy domains of interest: environmental protection, labor market regulation, and civil and human rights promotion. We consider a country as “treated” if it signs an agreement that includes the provision of interest in a given year.<sup>15</sup> Following the synthetic control and DID literature, we include a set of additional country characteristics to improve the matching between PTA signatories and control units. Annex Table A-3 A reports the basic summary statistics for the covariates included in the matching algorithm of the SDID. We include three measures of market size and wealth (GDP, population and GDP per capita), a measure of government accountability as a proxy for the likelihood a government will comply with international agreements (from the WDI database), the value of total exports and the share of total exports accounted for by the EU and the US, respectively. All else equal, greater trade dependence is likely to increase implementation of NTPs. Similarly, greater reliance on trade with the EU and the US (the two major proponents of NTPs globally) is also more likely to improve compliance.

# _3.2 Results: Non-binding provisions_

We first investigate the impact of non-binding NTPs on related non-trade outcome indicators. We compare outcomes in countries signing non-binding NTPs to those observed in countries that do not sign any agreements including the relevant NTPs (i.e., we exclude those countries that, at some point before or during the sample period sign a PTA with

> 14Most of the tests showing the consistency of SDID under
ownership of the mineral resources in oil extraction, gas and coalmining (representing 15 percent of total capital); (3) sector specific capital required for expansion of output in imperfectly competitive domestic firms producing either goods or services (representing 32 percent of the capital in the benchmark); (4) sector specific capital required for expansion of output in imperfectly competitive foreign firms producing either goods or services (representing 5 percent of the capital in the benchmark); and (5) ownership of licenses for monopoly rents in services sectors (representing 2 percent of capital in the benchmark). We do not have data that would allow us to associate specific capital holdings in given sectors with particular households. Thus, we assume that all households that hold capital, hold the different types of capital in the same proportions.

# **1.5 Household Consumer Demand**

We assume each household maximizes a Cobb Douglas utility function of the aggregate 35 goods in our model subject to its budget constraint (which is factor income net of transfers). Factor income shares, aggregated to the decile level, are reported in Table 4.<sup>2</sup> Each of the 35 aggregate commodities is a CES (“Armington”) aggregate of imported goods or services and goods or services produced in Russia. In imperfectly competitive goods sectors, imported and Russian produced goods are Dixit-Stiglitz aggregates of the outputs of foreign or Russian firms.

> **2** Households are modeled endogenously based on the 55,000 households of the Russian Household Budget Survey (HBS). The HBS, which is representative at the regional level, has very detailed information on household consumption expenditures, and information about age, gender, education, and occupation of each member of the household. It also has information about expenditures and savings and by implication household income. The major shortcoming of the HBS for our purposes is that it does not contain information on the sources of income of the households. For sources of household income, we
<!-- Start of picture text --> Figure 14. Costs and revenues over time ($/kWh)<br>a. Unit costs b. Average revenues<br>0.30 0.30<br>Predominantly diesel<br>0.25 0.25<br>Predominantly hydro<br>0.20 0.20 Overall<br>Predominantly diesel<br>Predominantly hydro<br>0.15 Overall 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>2001 2002 2003 2004 2005<br>2001 2002 2003 2004 2005<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

# **e. Do IBT structures allow for costs to be recovered?**

As noted above, IBTs are premised on the notion that surcharges on higher volumes of consumption will compensate for discounts on lower volumes of consumption, so that the utility breaks even overall. However, this outcome is contingent on the block sizes and associated price levels being correctly calibrated. It is often the case that the surcharges apply (if at all) to very high levels of consumption that are rarely reached in practice. In the case of Africa, the answer is more promising than might be anticipated.

A minority of countries have residential tariffs that recover costs independent of consumption levels, meaning that even the lowest priced block is priced high enough to recover costs (category I in table 8). (This is ideal from a cost recovery perspective, but raises questions as to whether these IBTs are also performing the intended social function of providing subsidized power to small consumers.) A second batch of countries has IBTs that succeed in recovering operational costs (category II), but will not—or are not likely to—attain total cost recovery based on their historical average household consumption.
We start by assessing the comparability of these different programs in terms of course content and who takes part in the training. We find there is considerable heterogeneity across studies in both the participants studied and in the length and content of the training provided, although a number of core topics are covered in most training sessions. We next discuss a number of challenges researchers have faced in measuring impact. Critically, most of the existing studies have very low power for detecting impacts on business profits or revenues. This lack of power results from the use of a single follow-up survey to measure impacts on relatively small samples of very heterogeneous firms. Power is further compromised because take-up rates are often far below 100 percent. In addition, many existing studies only look at impacts within a year of training, which may be too short a period to detect changes. Many studies also experience problems with attrition, selective survival and start-up, and non-responses on sensitive outcomes like profits and revenues. A final concern is that training may change measurement of outcomes, even if it doesn’t change the outcomes themselves, and so we discuss how several studies have attempted to show their results are robust to reporting issues.

With these issues in mind, we then assess what we have learned about the impacts of different programs on business survivorship and start-up, business practices, and profitability and enterprise growth. Among the minority of studies which have looked at impacts on survivorship of existing businesses, there is some weak evidence for a positive effect for male-owned businesses. However, for female-owned businesses training is found to have either zero or a slightly negative effect on survivorship. Stronger results have been found with respect to the impacts of training programs on new business start-ups. All the training programs studied which have content specifically intended to help people to start a new business have found training helps firms start-up, although there is some evidence that training may merely speed up the entry of firms who were going to enter anyway, and potentially change the selection of who enters.

Almost all
Libya and the timing of their journey towards their destination countries are influenced by changes in the news sentiment of migration-related articles in these same countries. Importantly, we document that these effects are significant only for some groups of migrants and under specific conditions, indicating an overall limited effect of a more negative tone of the news on migrants’ movements towards destination countries.

Relying on IOM data providing information on migrants located in 167 FMP in Libya for the period 2017-2020, we show that a more negative tone of migration-related news articles published in migrants’ destination countries increases their length of stay in Libya, slowing down their migration journey. To validate our findings, we provide evidence that the effect is significant only for migrants located in areas where it is more likely that they have internet access and that our results are not due to reverse causality. Our heterogeneity analysis shows that the effect is significant only for migrants coming from West African countries and located in the Western part of the country. Moreover, a worsening tone of the news has no effect in periods during which most of migration-related news articles have a negative tone or the economic conditions in destination countries are very good. Taken together, these results indicate that the effect of changes in news sentiment on migrants’ decisions is significant when, where, and for whom we expect it to be so. Yet, they also suggest a limited impact on overall migration movements.

> 34The negative and significant _ρ_ indicates that this is the case: an increase in the likelihood that _d_ is a preferred country corresponds to a reduction in the preference for _another_ country (for details, see Appendix D).

18
| POLICY RFSEARCH WORKING PAPFR 2076

# **Summary** findings

Using household data from five successive national surveys, Wodon analyzes the microdeterminants of (and changes in) consumption, poverty, growth, and inequality in Bangladesh from 1983 to 1996.

Education, demographics, land ownership, occupation, and geographic location all affect consumption and poverty. The gains in per capita consumption associated with many of these household characteristics tend to be stable over time.

Returns to demographics (variables in household size) have the greatest impact on growth, perhaps because of improving employment opportunities for women.

Education (in urban areas) and land (in rural areas) contribute most to measures of between-group inequality. Location takes second place, in both urban and rural areas.

Wodon introduces the concept of conditional between-group inequality. Existing group decompositions of the Gini index along one variable do not control for other characteristics correlated with that variable. Conditional between-group Ginis avoid this pitfall. He also shows how to use unconditional and conditional between-group Ginis for simulating policies.

This paper-a product of the Poverty Reduction and EconomicManagementSector Unit, SouthAsia Region-was written as part of background work for the Bangladesh poverty assessment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jillian Badami, room MC10-336, telephone 202-4580425, fax 202-522-2428, Internet address jbadami@worldbank.org. Policy Research Working Papers are also posted on the Web at http:i//www.worldbank.org/html/dec/Publications/Workpapers/home.html.The author may be contacted at qwodon@7worldbank.org. March 1999. (33 pages) _The Policy Research Workong Paper Series disseminates the findings of work in progress to encourage the exchange of ideas arolit develo pment issuies. An objective of the series is to get the findings out ,auickly, even if the presentations are less than fuilly polished. The papers carry the names of the autbors and sbould be cited accordinzgly. iDbe findings, interpretations,
_Cost-recovery tariffs do not look to be affordable for the majority of the population in Uganda; but more so for the relatively affluent minority with access to services._ With a cost-recovery tariff of $0.24 per kWh and a monthly subsistence consumption of 50 kWh, the associated utility bill would come to $12 per month. Based on the distribution of household budgets in Uganda, monthly utility bills at these levels would be affordable by only 10 percent of the population (figure 31). Similarly, a bill for subsistence consumption of 10 m<sup>3</sup> of water would come to $12 per month. A more limited level of subsistence consumption of 25 kWh per month for power and 4 m<sup>3</sup> per month for water—which is capable of meeting the most basic needs—would cost $6 and $5 per month respectively, but would still only be affordable to barely half of the population. Nevertheless, it is important to recall that access to power and piped water services in Uganda at present is very limited and largely confined to the top quintile of the budget distribution. As a result, among those that have access to the service today the majority should be able to afford a subsistence level of consumption of these services. Affordability will become a serious issue as Uganda accelerates coverage of utility services, however. (Given that this analysis is based on the 2002 Household Expenditure Survey, the affordability situation is likely to have improved significantly in the meantime given sustained economic growth.)

**Figure 31. Affordability in Uganda worse than in other low-income countries, 2002**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Uganda<br>LIC<br>Average cost of power is
This comparison does, however, illustrate that replicating the lineup procedure used to generate the published estimates is not necessarily straightforward, and that the nature of the line-up method usually has small but noticeable impacts on the profile.

**Table 8: Sensitivity to line‐up method**

||**Method 1 (Use published**<br>**estimates)**|**Method 2 (Adjust welfare**<br>**andpopulation)**|**Method 3 (Adjust**<br>**population only)**|
|---|---|---|---|
|Percent of extreme poor in<br>rural areas|80.1|80.0|80.3|
|Percent of poor working<br>adults in agriculture|64.6|62.7|63.1|
|Percent of extreme poor<br>being0‐14years old|44.2|43.0|42.5|
|Percent of poor adults with no<br>education|39.1|37.4|37.7|
|**Methodological details**||||
|Year|2013|2013|2009‐2014 mixed|
|Poverty lines|Derived from PovcalNet<br>povertyrates|$1.90 and $3.10|$1.90 and $3.10|
|Welfare aggregate<br>adjustment|Adjusted using national<br>account data on real<br>consumption or GDP<br>growth|Adjusted using national<br>account data on real<br>consumption or GDP growth|None|
|Population||||

26
level, reinforcing the above analysis. There is no reason to expect the consumption patterns across quintiles to differ significantly given the large sample.

# **_Multiple connections_**

The final potential concern is that households may have multiple connections, spreading their consumption across meters and thereby understating their total consumption. Approximately 83 percent of the matched sample has a single meter connection (that is, the bill payer’s CNIC appeared only once in the database), 6 percent have 2 or 3 meters, and 11 percent have more than 3 meters (see Appendix, part A). For the analysis we removed all households with duplicate observations, since it is not clear whether this reflects an effort on the part of households to evade higher tariffs or is simply an error in the CNIC records. However, even if wealthier households are using multiple meters, they qualify (illegally) for subsidies despite having higher overall electricity consumption. Thus this would not affect our finding that the bulk of subsidies go to non‐poor households.

> 11 Results are available upon request.

9
additional percentage point of forced displacement as a share of the municipal population of 1990.

To gauge the aggregate magnitude of these effects, note that a municipality which lost 14 percent of its 1990 population to Colombia (the average implied by UNHCR figures) would experience a decline of 39.3 percentage points in electoral turnout (= 2.81 _×_ 14) and 21.1 percentage points in opposition support (= 1.51 _×_ 14). These effects are large enough to alter electoral outcomes decisively.

Table 2: Forced Displacement and Electoral Outcomes

||Tur|nout|Oppo|sition|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Panel A: OLS**|||||
|Migration Outfows|-0.425***|-0.325**|-0.197***|-0.136**|
||(0.145)|(0.132)|(0.054)|(0.055)|
|**Panel B: 2SLS**|||||
|_Second Stage_|||||
|Migration Outfows|-2.806***<br>(0.924)|-2.815***<br>(0.923)|-1.505***<br>(0.451)|-1.515***<br>(0.450)|
|_First Stage_|||||
|Imputed Outfows|0.206***|0.207***|0.206***|0.207***|
||(0.054)|(0.053)|(0.054)|(0