rafmacalaba/data-use-mentions-extended huggingface dataset:
of people they had to mobilize, and the number of groups that they had to create. They also achieved these targets more quickly than their local counterparts (Table 3, Panel C).

In our fieldwork, we found that external facilitators used their status as “outsiders” to create new systems of working. A salient aspect of this is that they often chose to work late at night, sometimes holding meetings until 2AM, almost unaware of the dangers of doing so.<sup>26</sup> We observed that the Rajasthani culture of hospitality towards outsiders often served to benefit the external facilitators. There was a great deal of curiosity about them, and their distinctive language, appearance and diet, made them highly visible in a way that local women were not. In our qualitative work, we found mixed opinions about whether this was an advantage or disadvantage, but most agreed that external facilitators found it easier to get the attention of villagers and develop an initial interest in the SHG program.

# Differences in Impact

Self-Reported Measures of Impact In our facilitator survey, we asked respondents to provide examples of the impact of their mobilization. Specifically, we asked them to describe an example of collective action the women had taken that made them most proud (as mobilizers).<sup>27</sup> The response was left open-ended and we post-coded the responses into the following groups: woman-specific issues (dowry, domestic violence, women’s credit groups, child-care issues, etc.), political issues and issues related to public service delivery. To test the hypothesis that external facilitators induce collective action differently than internal facilitators, we ran the following regression: where cfbis a measure of collective-action outcomes reported by facilitator _f_ in block _b_ , EXTERNALb is a dummy variable that takes value 1 if the SHGs in the block are being facilitated by external facilitators, Xf is a vector of controls for facilitator _f_ (this includes her age, education, experience, job-description and salary), and εibis the error
|Southern|Swaziland Electricity<br>Company|31-Mar|Electricity<br>only|Yes|Yes||
|**Tanzania**|2014|Eastern|TANESCO (Tanzania<br>Electricity Supply<br>Company)|30-Jun|Electricity<br>only|Yes|Yes|TANESCO changed its fiscal year so in the case<br>of Tanzania an 18-month period is covered in<br>the analysis reflecting the latest financial<br>statements available covering the period Jan<br>2014–Jun 2015.|
|**Togo**|2013|Western|CEET (Compagnie<br>Energie Electrique du<br>Togo)|31-Dec|Electricity<br>only|No|No|Majority of grid power is purchased from CEB,<br>a regional utility supplying Benin and Togo.|
|**Uganda**|2014|Eastern|Umeme|31-Dec|Electricity<br>only|Yes|Yes|Umeme accounts for 94% of end-user sales in<br>the reference year. Balance of sales are done<br>by UETCL (exports) and small rural<br>concessions.|
|**Zambia**|2014|Southern|ZESCO|31-Dec|Electricity<br>only|No|Yes|ZESCO FY-end changed from Mar 31 to Dec 31<br>in FY14.|
|**Zimbabwe**|2012|Southern|Zimbabwe<br>Transmission and<br>Distribution<br>Company|31-Dec|Electricity<br>only|No|Yes||

_Source_ : World Bank staff’s review of utility data and publications. _Note_ : FY = financial year; MYTO = Multi-Year Tariff Order (not a utility); UETCL = Uganda Electricity Transmission Company Limited.

84
tariffs thanks to the government’s subsidy policy, and peak demand rose by 5.1 percent per annum between 1998 and 2016.

**Figure 4:  Installed generation capacity (GW), 1990-2017**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> Source: NEPRA, State of Industry Report (various issues); and Pakistan Bureau of Statistics, Census of Electricity Establishments (various issues).

The ownership of generation capacity by type in 2017 is shown in table 3 and the importance of IPPs is evident. The private ownership of generation increased steadily from a zero share of total generation capacity prior to 1994, when IPPs were first allowed to enter the sector, to 31 percent in 2006 just prior to the privatization of KE, and to 38 percent in 2017.

**Table 3:  Generation capacity by ownership type in 2017 (MW)**

|**Ownership type**|**Capacity (MW)**|**Share of total capacity (percent)**|
|---|---|---|
|WAPDA hydel|6902|24|
|IPP hydel|214|1|
|Thermal GENCOs with PEPCO|5897|21|
|KE own thermal|1874|7|
|IPPs connected with PEPCO|10566|37|
|IPPs connected with KE|252|1|
|CPPs/SPPs connected with KE|87|0|
|Nuclear|1142|3|
|Renewable connected with PEPCO|1465|4|

24
urbanization may, however, affect the program placement decision.<sup>6</sup> This opens up the opportunity to use rankings as identifying variables (as opposed to levels). Imagine that urbanization is highest in area A, followed by B and C, and that the more urbanised an area is, the more likely it is to receive a program. Identification is achieved because the _rank_ of an area primarily depends on other areas’ absolute value of the ranked variable. Specifically, assume that

> 1There are also additional problems with using fixed effects, such as measurement error bias. For a discussion of this and other problems in the study of family planning see, for example, Angeles, Guilkey and Mroz (1998). 2Also using cross-sectional information, Miller (2010) found that Columbia’s family planning program, Profamilia, reduced lifetime fertility by around half a child, equivalent to only 10 percent of the the sharp decline in fertility over the period the program was implemented. As the process of allocating programs were assumed to be arbitrary, these results may, however, be under- or over-estimates.

> 3For Indonesia, Angeles, Guilkey and Mroz (2005) report using the same approach, but found no evidence of non-random placement of family planning programs.

> 4The approach closest to ours is by Pitt and Menon (2010), who used average characteristics of other areas, such as education level, for their instruments. A potential issue with their approach is that if network effects are important these averages might not serve as valid instruments.

> 5The cost of children may be higher in more urbanized areas reducing fertility.

> 6It may, for example, be less costly for the government to place programs in areas that are more urban because of easier access.

2
br>Development Indicators|
|Independent<br>Variables for|Remittances|Remittances|Personal remittances, received (% of GDP)|World Bank World<br>Development Indicators|
|<br>annual cross-<br>sectional<br>i|i|Monetary Freedom<br>Index|Monetary freedom combines a measure of inflation<br>with an assessment of various government activities<br>that distort prices. (100–free, 0-repressed)|The Heritage Foundation|
|regressons|Economc<br>Freedoms|Financial Freedom<br>Index|Financial freedom is an indicator of banking<br>efficiency as well as a measure of independence from<br>government control and interference in the financial<br>sector. (100–free, 0-repressed)|The Heritage Foundation|
||ICT deeloment|Cell Phone<br>Subscriptions|<br>Mobile cellular subscriptions (per 100 people)|World Bank World<br>Development Indicators|
||vp|Broadband<br>Subscriptions|Fixed broadband subscriptions (per 100 people)|World Bank World<br>DevelopmentIndicators|

41
the CMA measures and the error term.<sup>4950</sup>

# **5.4 Labor and Capital Wedges**

Labor and capital wedges are a crucial parameter for the quantitative analysis. I follow the popular approach from Hsieh and Klenow (2009) and use the inverse of the wage bill and capital share to calibrate the distortions.<sup>51</sup> From the profit-maximization condition, the inverse of the labor and commercial floorspace share paid by each firm is where _wislis_ is the wage bill, _qiszis_ is the commercial floorspace payments, and _pisyis_ are total sales or value-added. I can observe the left-hand side of this equation for each firm in the Economic Census, and use the average labor share _βs_ and markups in each industry to calibrate the wedges. To aggregate from the firm level to the census-tract-sector cell, I take the mean of the inverse of the

> I am controlling for this variable after taking the ratio between the formal and the informal sectors since this wage index cancels out. Then, the reallocation of workers does not bias the estimation of _κ_ since what matters is the share of formal to informal residents across census tracts even if there is migration.

> 49I didn’t instrument the change in CMA with the two instruments since both of them capture changes in commuting cost because of the transit shock.

> 50Relative to previous studies on estimating labor supply elasticities across sectors, such as Galle et al. (2017), Lagakos and Waugh (2013), and Berger et al. (2019), my estimates are similar.

> 51Other papers such as Busso et al. (2012) and Levy (2018) that have explored the role of resource misallocation in Mexico also use the same method.

30
domestic aggregate demand to be limited.

To investigate this issue further, I match up the project‐level data on disbursements with contract‐level data on individual procurement contracts awarded on World Bank projects. The World Bank’s procurement records include information on the country of origin of the supplier to which each procurement contract was awarded, as well as, of course, the total value of the contract. I aggregate these individual contracts up to the project level, and construct the share of total procurement contract value associated with a project which is tendered to suppliers situated in the country where the project is located. This is only an imperfect indicator of where disbursements on World Bank loans are spent, for at least three reasons. First, this information is available only as an aggregate for the entire project, and not for the individual year‐over‐year disbursements. Absent better information, I make the 20
# 6.4 Expected returns to education vary widely.

Officials were asked about perceived income levels of similar individuals with different amounts of education (primary, secondary, TVET or tertiary). By comparing their responses from one level of education to another, we can infer how officials foresee returns to different amounts of education.

Overall, perceived returns to secondary education look similar in EAP and non-EAP countries. Perceived returns to tertiary education however are lower in the region than outside of it (Figure 19). Expected returns are much larger at the tertiary level but differ significantly for all countries in the region (across primary, secondary and tertiary completion).

_Figure 19: Differences in education premium envisioned by level and country._ <!-- Start of picture text --> Non EAP<br>EAP average<br>VNM<br>PHL<br>MNG<br>LAO<br>IDN<br>0% 20% 40% 60% 80% 100% 120% 140% 160% 180%<br>Tertiary vs. secondary education completed<br>Secondary vs. primary education completed<br><!-- End of picture text --> Source: CDG–World Bank 2022 survey.

# 6.5 Officials have diverse views on education premiums and substantially overestimate returns to tertiary education.

Actual income gains for individuals who complete secondary education versus those who only complete primary education globally were 51 percent on average (Patrinos and Montenegro, 2014), while the anticipated premium estimated by respondents for completing secondary education ranged from 38 to 63 percent, for an average of 48 percent. Given that the available data on actual returns are not updated, the estimates for returns to secondary education completion are quite close on aggregate. However, there are considerable differences between countries. For example, Lao PDR respondents estimate the returns to secondary by 20 percentage points above the global average, while respondents in the Philippines estimate 25 percentage points below the global average.

According
point see, for example, Krueger and Lindahl (1999).

> 14As in most of the recent literature, we use the World Tables CD-ROM as a data source and define natural resources exports as the sum of exports of fuels and nonfuel primary products.

15We replicate the measure of terms-of-trade shock developed by Easterly, Pritchett, and Summers (1993). See appendix B.

> 16For a detailed discussion of the control variables see Sachs and Warner (1995) and Temple (1999). In our empirical specification we do not rule out the conditional convergence hypothesis; hence we include the lagged value of income per capita. Given the theoretical framework, it may be possible to recover conditional convergence to a given growth rate after including a decreasing marginal return to capital.

> 17Because in our model the economy exports all of its output, we can use either the share of natural resources exports in GDP or the share of natural resources exports in total exports as the proxy for resource abundance. See equation 30

17
compared to US$362 a decade earlier, according to figures from UN Comtrade. A key trade policy question therefore is how growth in textiles and apparel can be reinvigorated to boost the country ́s exports base. Key among possible measures is the reduction of tariffs and para‐tariffs on intermediate goods to reduce production costs of domestic firms and promote adoption of cheaper or better quality inputs from wherever they are most efficiently sourced. In parallel, this should be combined with a gradual reduction in these sectors’ output tariffs to lower effective protection levels accorded to these sectors, which will reduce the overall anti‐export bias and promote a greater outward orientation of firms active in these sectors.

We use UTAS to assess the impact that such a strategy would have on the textiles and wearing apparel sectors and rely on data from GTAP to account for the production structure in these sectors in Nepal. Data on import duties are taken from the UN TRAINS. For simplicity, all our simulations are based on the product homogeneity modeling framework of UTAS, which assumes a complete tariff pass‐ through on domestic prices.<sup>11</sup> The following policy questions guide our exposition. Screenshots of UTAS are included to facilitate a step‐by‐step replication of our results in UTAS.

### Q1: Which tariffs have the greatest impact on upstream production cost?

The _Tariff‐ranking_ module allows users to establish which tariff lines at the HS 6‐digt level have the largest impact on a particular sector. We use the module to determine the 20 tariffs with the highest impact for the apparel and textiles sectors, respectively. In the _Tariff‐ranking_ module this is done by clicking on the ‘Change Data’ icon and selecting the appropriate sector and the number of tariff lines to be considered in the analysis (see Figure 5).

> 11 Less restrictive assumptions are considered in the example of Section 5.2

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productive. This section demonstrates how TFP at the firm level is related to GDP per capita at the country level, and compares the level of TFP in Serbian firms with the levels in comparator countries. These comparisons use the large PICS-BEEPS dataset of about 27,000 firms from 50 countries.<sup>3</sup> TFP is a multi-factor productivity measure that represents the efficiency of the firm in transforming factor inputs into outputs. A firm that has a high level of TFP is one that can produce a high level of output for given quantities of capital and labor. TFP can be affected by wide range of factors: technology, managerial quality and incentives, corporate governance, government policies, and, of course, various dimensions of the investment climate. TFP is usually analyzed in a production function or growth 4
. The DHS index, defined as ~~� ]~~ , �.� ��������� where, X� is the number of employees at the period t. This index measures the growth rate of the firm relative to the average size. HGF definitions based on this method also select a given share of firms in the top percentile of the index distribution.<sup>9</sup> In this paper we focus on 3 definitions of HGF including: i) a variation of the OECD definition; ii) the Birch definition; and iii) the DHS definition, as described in table 1. However, due to the large number of small firms in developing countries, we do not impose a threshold on a minimal number of employees. In Tunisia, the median firm has 2 employees. For the sake of space and being concise with some of the descriptive statistics, whenever we present a table only with 1 definition, we use HGF1.

<u>Table 1 – Definition of HGF based on a three-year period of cumulative growth</u>

|**Definitions**|<br>**Description**|**Based on**|
|---|---|---|
|HGF1|Firms with average annualized growth greater than 20% per annum.|OECD|
|HGF2|Firms in the top 10 percent of the Birch index, over a three-year<br>period.|Birch|
|HGF3|Firms in the top10percent of DHS Index.|DHS|

Source: Authors elaboration.

> 9 Another option suggested by Clayton et al. (2013) classifies a firm with fewer than 10 employees as an HGF if it made a growth of 8 or more employees over a 3‐year period.

4
(Merten and Haller 2008). Without countrywide data on extents of land under customary tenure, coverage with formal or informal documents, perceived tenure security and ability to transfer, assessing representativeness of case studies, defining the objectives to be achieved by the new land policy or ways in which they should be tracked is difficult and adding the SDG module to the LFS provided a cost-effective first step towards closing this gap.

# **3. Data and descriptive statistics**

To illustrate how integrating the SDG land tenure module into ongoing surveys can inform the policy debate, we describe descriptive results, noting that less than half of households can bequeath land to their offspring or sell it; only 11% have title; with 43% concerned about losing land within the next 5-years, levels of perceived tenure insecurity are high; and the share of informal documents (14%) outnumbers that of formal ones and 54% of those without formal document would want to acquire title and pay more than a systematic process based on streamlined regulations and implemented efficiently would cost.

> 9 Before 2000, retired or retrenched civil servants accounted for a significant share of land acquisition whereas after 2000, speculative land acquisition by those who made a fortune outside agriculture seems to predominate (Sitko and Jayne 2014). 10 “Land titling may be implicated in process of elite capture of land at the possible expense of future small-scale farm growth and farm consolidation” (Sitko and Jayne 2014, p. 199) so that growth of the emergent farm sector is not a reflection of small-scale farmers transitioning towards higher productivity, investment (capital accumulation), commercialization, and job creation, but largely due to policies that allow disproportionate capture by a rural minority with little or no effect on agricultural growth.

9
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improving the organization, presentation, consistency, and general accessibility of project data. Availability of the relevant historical data on a consistent format cross different projects, and over reasonable periods of time, would enable statistical analysis at a much more sophisticated level than has been possible In this study. This Is highly desirable, since the Bank would then be In a position to better understand the underlying causal factors that Influence pro;3ct and utility performance. Such Information would be Invaluable In designing loan covenants, arresting adverse trends at a much earlier stage In the project cycle, and predicting power project and utility performance. While most of the useful Information on past and current lending may be ultimately retrievable, the efort Is likely to be prohibitively cnstly. Therefore, It Is recommended that while the comprehensive database compiled for this study be further developed, regularly updated, and made readily accessible to other Bank staff, a serious eftort be made to also collect and preserve all relevant data, for current and future power sector projects.

Beyond the data collection and organization level, there Is a need to build up and maintain a critical mass of staff who can provide Intellectual leadership and proactive guidance, by analyzing past Information, drawing useful lessons for the future, and developing new and viable policies for future operations. This problem Is compilcated by the tendency for the deep insights and knowledge that has been acquired by senior staff to vanish with their retirement. At the same time, daily pressures on operational staff act as a deterrent to those who wish to synthesize and preserve some of their valuable experience for the benefit of colleagues. The strengthening of the policy development capability Is particularly Important, given that Bank lending Is presently carrled out In an environment of rapid change and many relatively Inexperienced operational staff are being called upon to address more and more complex Issues, often under severe time and resource constraints.

Beyond the database development mentioned above, It would be useful to develop an agendr. ut more in-depth follow-up Bank studies In areas such as
For instance, one such group could be composed of the share of _girls_ aged 0 to 15 years old with _access to water supply_ who lived in _rural_ areas in _Antioquia_ , Colombia, based on the census of 1985. Then, we calculate the household income of the same subgroup 30 years later—women from 30 to 45 years old—based on the household survey of 2015. We perform the same procedure for all the possible combinations of subgroups in 12 countries in LAC. The results reflect a strong correlation between past access to services and education and current household income levels.

Figure 6 shows the relationship between primary completion in the 1980s and household income in 2015 (circa). For some countries like Bolivia, Brazil, Colombia, Ecuador, Mexico, and Peru, the relationship is remarkable. Subgroups with lower primary completion rates in the 1980s have consistently lower household income in 2015 than those with higher primary completion rates. In addition, this relationship is stronger in the tails of the distribution of primary completion rates. Bolivia, for instance, shows clear differences in this opportunity between rural and urban areas. Primary completion in urban areas are on average twice as large as the former rural ones and, consequently, their median household income is also higher. In other cases, like Brazil or Mexico, the distinction is clear in the tails but not at the center of the distribution. In both countries, the distribution of the share of primary completion starts close to zero and goes up to 60 percent. For those subgroups at the very bottom and at the very top of the distribution their corresponding household incomes several decades later are associated with the starting point of primary completion. However, the variability of household income levels of the subgroups at the center of the primary completion distribution is wider and not clear relationship is found.

For other countries, like Dominican Republic, Chile, and Uruguay, the relationship between primary completion and household income 30 years later is not clear. For these countries, the initial 23
# **Simulating Aggregate and Distributional Effects of Minimum Wage Increases in Romania: Evidence from Survey and Administrative Tax Data**<sup>*****</sup>

Monica Robayo-Abril<sup>1</sup> Mădălina Zamfir<sup>2</sup> Marcin Wroński<sup>3</sup> 2 Romanian Ministry of Finance 3 World Bank and SGH Warsaw School of Economics

**JEL classification** : E24, J31, J38, J46, J48

**Keywords** : labor market; minimum wage; administrative tax data; EU-SILC; Romania

> ∗ This paper was prepared as part of a collaboration between the World Bank and the Ministry of Finance in Romania to analyze minimum wage scenarios. The paper was part of the Poverty and Equity Program for Romania in the World Bank's Global Poverty and Equity Practice. The authors are grateful for comments received from Georgiana Balaban (Romanian Ministry of Finance), Reena Badiani (Senior Economist, World Wank) and Mihai Macovei (Economic Analyst, European Commission, Directorate-General for Economic and Financial Affairs).