rafmacalaba/data-use-mentions-extended huggingface dataset:
firms active in this sector. We observe 4-digit NACE codes identifying the branch of the economy. Unfortunately, our data does not directly identify public-sector workers. However, official statistics indicate that only a small proportion of public employees fall into the category of minimum-wage earners.

**Nonetheless, employing the administrative tax datasets to examine employment and unemployment analysis encounters certain obstacles.** Firstly, the data is primarily intended for administrative purposes, such as computing social security contributions and worker benefit eligibility, rather than designed for research purposes. Secondly, there is no registered unemployed in the data. It differs from the unemployment definition employed in administrative tax datasets, even if it exists. It diverges from the standard set by the International Labour Office (ILO), as it does not contain the entire universe of unemployed (only those registered), and tracking of the unemployed is limited to the period during which they receive benefits. In Romania, the unemployed do not pay labor income tax and thus are absent in our dataset. Therefore, our analysis focuses on formal employment stocks and flows rather than unemployment.

**We exclude missing values from our analysis, as there is insufficient information to impute these values accurately.** The gross salary and working time information is missing or equal to zero for some workers. We cannot use such observations in our analysis, as the reasons for the missing variables are unknown. If there is a systemic bias in missing variables (e.g., gross salary information is often missing for low-income earners), our results may also be biased. The share of taxpayers included in the sample (observations with non-missing information on working hours and gross wages) varies between 72% and 87%.

**Our first task is to identify minimum wage workers in the tax administration data.** We identify minimum wage earners based on the value of the gross wage. As discussed above, in January 2020, three different minimum wage levels existed in Romania. We can directly identify construction
Households with uneducated heads are significantly more likely to be poor or vulnerable. Middle class households appear to be generally further away from agricultural employment and the likelihood of being in the middle class is highest among Nepalis who are in salaried professional employment or in other forms of self-employed services. Vulnerability on the other hand is highest among those self-employed in agriculture and service sector employment, most likely in blue collar occupations. (See Figure 7.) Overall, poverty and vulnerability are highest among households deriving their livelihoods from wage employment in agriculture. Among the various caste/ethnic groups, middle class prevalence is highest among Newars, followed by Brahmin/Chhetris and Hill Janajati groups. Dalits have the lowest middle class prevalence. Vulnerability on the other hand is highest for Muslims and other minorities, Terai Janajatis and Terai Middle Castes and lowest among Newars. (See Figure 8.)

**Figure 7: Distribution of the Poor, Vulnerable and the Middle Class in 2011 by Education and Occupation of the Household Head**

<!-- Start of picture text --> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Agri Self 35 49 15<br>Servi. Self 21 30 42<br>Agri Employ 58 38 3<br>Professional Employ 8 31 54<br>Other services Employ 33 46 20<br>0<br>No schooling 42 46 12<br>11+ years of schooling 7 29 57<br>Poor Vulnerable Middle class Upper class<br><!-- End of picture text --> Source: World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11

**Figure 8: Distribution of the Poor, Vulnerable and the Middle Class in 2011 by Household Head Caste and Ethnicity**

13
|Constant|-4.087***|-6.878***|-1.612***|-0.888***|-1.386***|-0.441|3.019***|2.510***|
|---|---|---|---|---|---|---|---|---|
||(0.523)|(1.232)|(0.189)|(0.318)|(0.402)|(0.735)|(0.430)|(0.802)|
|Observations|4905|4874|4905|4879|1430|1313|1381|1138|

Source: Authors’ estimation using RLMS, ESS and LiTS for year 2016.

Note: This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their work conditions, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. Dependent variables in columns (1)(4) are from the RLMS, (5) and (6) from the LiTS and (7) and (8) from the ESS. All dependent variables are defined as binary. The LiTS does not include information on occupation and the ESS does not include a location variable for urban or rural. All specifications include region dummies. Establishment size of place of work is reported in three categories in the RLMS and the LiTS (under 10, 11-100 and 100+) and in five categories in the ESS (under 10, 10-24
- 15 -

||Year|**Tabl**|**e 3. Samp**|**le covera**|**ge by mon**|**th**|||
|---|---|---|---|---|---|---|---|---|
||200|2|200|3|200|4|200|5|
|Month|Obs.|%|Obs.|%|Obs.|%|Obs.|%|
|4|29|4.5|||||||
|5|324|50.5|569|48.2|648|48.7|1,058|65.8|
|6|244|38.0|594|50.3|668|50.2|538|33.5|
|7|45|7.0|17|1.4|16|1.2|12|0.8|
|Total|642|100.0|1,180|100.0|1,332|100.0|1,608|100.0|

Source: Albania LSMS data.

In Albania the climate differs from year to year and depends on location. This variation is, of course, of particular importance to identify the climatic impacts on the electricity demand in our empirical model. In the sample period, the month of June in 2003 was hotter than usual. In that year, air conditioners and other seasonal home appliances may have contributed to increasing the residential electricity demand. In addition, climate conditions vary significantly across region in the country. The northern and eastern mountainous regions are relatively cool in Albania. By contrast, the coastal and southern areas are hotter (Figure 5). In 2003, temperature was high, exceeding 30
potential determinant for migration to explore other potential determinants that have not been established in prior literature or that are not commonly collected in other surveys, such as individual perceptions of success and means to achieve success. We use virtually all variables collected in the survey.<sup>6</sup> We end up with 301 continuous or binary potential determinants that will be fed in as inputs to the ML algorithms. The full list of potential determinant variables used in our analysis is presented in Appendix Table 1.

Our sample is composed of 5,131 men and 8,187 women. The individuals here are what we consider as “potential migrants”. Due to the random selection of an individual within the household, the

> 3 Of the households in our survey, 96.1 percent reside in rural areas, compared to only 44 percent of households residing in rural areas in a recent nationally representative socioeconomic survey (SUSENAS 20192020).

> 4 Ideally, each household should have a corresponding individual representative. However, in practice, the household and individual surveys have to be conducted on two separate occasions due to the length of the survey. There are a few attritions (0.1 percent), where the randomly selected individual cannot be followedup. Since the interest to migrate question is asked in the individual (not household) survey, we can only use the balanced sample with both household and individual surveys completed (N = 13,318).

> 5 Each category of categorical variables is recoded as separate binary variables.

> 6 We exclude irrelevant questions or questions with no interpretable meaning for our analysis. This includes questions such as the time and day of the interview.

8
<!-- Start of picture text --> Figure 26: China-US Bilateral Trade<br>and Balance on Current Account<br>200 125<br>100<br>120<br>0<br>115<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-100<br>110<br>-200<br>105<br>-300<br>100<br>-400<br>-500 95<br>Exports of Goods and Services and Income receipts<br>Imports of Goods and Services and Income Payments<br>Balance on Goods and Services<br>Balance on Current Account<br>RMB Real effective exchange rate (right axis)<br>2005=100<br>US$, billions<br><!-- End of picture text --> Sources: Bureau of Economic Analysis; IMF, International Financial Statistics 44. Second, if indeed the undervaluation of the RMB was the main reason for the rising U.S. trade deficit and China‘s trade surplus, the trade deficit of the United States of other countries that compete with China should have declined. However, most other developing countries also increased their current account surpluses and reserves substantially in the same period (see figures 27 and 28 below).

33
Table 3 (Contd.)

# Wage Premium for Formal Sector Jobs

|Study|Estimation<br>technique|Burkina<br>Faso|Cameroon|C6te<br>d'Ivoire|Mali|
|---|---|---|---|---|---|
|Vallee and<br>Thomas<br>(1994; Table<br>5.12, page 170)|OLS<br>with sample<br>split between<br>regulated and non-<br>regulated firms||34.3 %<br>[RPED, 1993;<br>N = 740]|||
|Vijverberg and<br>Van der Gaag<br>(1993b; Table<br>6.2, page 34)|OLS<br>with factor<br>analysis<br>for job<br>coverage|||65.2 %<br>[CILSS, 1985;<br>N = 295]||

- Note: All studies control for individual characteristics, but the set of controls varies across studies. The wage premium is based on the coefficient (c) multiplying the formal sector dummy variable. In the case of Lachaud (1993b), it is based on the difference between coefficients for covered and uncovered wage jobs. In the case Vijverberg and Van der Gaag (1990), the formal sector dummy is the first of the principal components of a set of job characteristics. For large values of the c coefficients, in absolute terms, the percent premia is approximated as 100[exp(c)-1]. Data sources and sample sizes are reported in brackets. CILSS is the Cote d'Ivoire Living Standards Survey, a household survey with national coverage. IIES is a series of labor force surveys carried out by the Institut International d'Etudes Sociales in the capital cities. RPED is the Regional Program for Enterprise
|Sector Dummies|Yes|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service.

**_Note:_** Table report Probit estimate on the probability of a firm being a monopsonit. _⋆_ The capital city, Accra, is used as the base variable. Robust standard errors in parentheses _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1.

38
Table 1 offers a partial list of _some recent surveys_ done within the Bank. The table reveals four main types of surveys: (1) Comprehensive surveys (RPED, ICS); (2) Surveys on transition (Russia: Economic and Civil Society; Corporate Governance in Poland, Hungary, and Czech Republic; Large Scale Privatization in Mongolia; The Emergence of Private Sector Manufacturing in Poland, in Hungary; Industrial Enterprises and Adjustment in Russia; Private Service Firms in Russia-St. Petersburg.); (3) Surveys of the market environment, including regulation and govemment-industry relations (World Business Environment Survey); and (4) Topic-specific surveys, such as on training (Enterprise Training in Developing Countries, Tan and Batra), or on SME problems (CECPS, "Small-and-Medium Industry Impact Evaluation," 1995; and "Enterprise training in developing countries," Tan and Batra).

How well do World Bank surveys meet the data needs outlined above? We begin to address that question below. One lesson from the Bank experience is that surveys are extremely difficult. Thus, while we make the comments below with the ideal data set in mind, we recognize that in practice surveys may not be able to meet this ideal. Longitudinal surveys are costly and take too long for many Bank operations. Some data, meanwhile, simply do not exist. Nonetheless, it is instructive to consider what would have made these surveys more useful, and use that knowledge when designing future surveys.

**1. Data consistency:** Although World Bank surveys are usually designed to address a particular issue, each could be more broadly useful by consistently including a small set of standard questions on firm performance, such as total output and profitability. The survey should also collect price data on the firm's main products, inputs, and investment goods. In addition, the survey becomes more valuable when it contains enough information to estimate production functions, including value added, number of employees, capital stock, and material inputs.
such as when developing global poverty scenarios for 2030 (see Jolliffe et al., 2014 for details). The results indicate a stronger correlation between growth in surveys and national accounts for longer and more comparable growth spells. For consumption means in surveys, we observe that most coefficients are significantly less than one, suggesting a lower pass through of growth from national accounts to surveys, than for income means in surveys where the coefficients are larger and the difference from unity mostly statistically insignificant. The Middle East and North Africa region stands out as a region with particularly poor correlation between national accounts and survey growth rates. We find a much stronger relationship in Eastern Europe and Central Asia than Ravallion’s (2003) assessment, which mainly used data from the transition period of the early 1990s, when both national accounts and survey data were particularly poor and periods of high inflation gave large measurement errors.

The sample of comparable growth spells available in the Global Database of Shared Prosperity, which is designed to assess growth in comparable household surveys over spells of 3 to 7 years, shows clearly how large the difference in annualized growth rates can be even for modest spells length with high quality and comparable survey data. For the most recent version of the database covering spells from approximately 2013 to 2018, the gaps between annualized real growth rate in survey mean and HFCE mean range from -8.9 to 6.7 percentage points, with mean gap of -0.27 percentage points, but not statistically significantly different from zero at 95%-confidence level. When using the no-constant regression approach to assess the gaps with the Shared Prosperity Database, the regression coefficient is 0.86 for GDP (not statistically significantly different from 1) and 0.81 for HFCE (statistically significantly different from 1).

# **4. Implications for Global Measures of Poverty and Inequality**

With the existing literature on why the gaps exist and the empirical evidence on the size of the gaps in mind, we consider two approaches for
ETHIOPIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has collected and analyzed extensive infrastructure data for more than 40 Sub-Saharan countries, including Ethiopia. The results are presented in reports on various infrastructure sectors—ICT, irrigation, power, transport, water and sanitation—and policy areas, including investment needs, fiscal costs, and sector performance.

This country report presents the key AICD findings for Ethiopia. This will allow its infrastructure situation to be benchmarked against that of other African nations that, like Ethiopia, are low-income countries, with particular emphasis on immediate regional neighbors in East Africa.

Several methodological issues should be borne in mind. First, the cross country nature of the data collection creates an inevitable time lag. The period covered by the AICD runs from 2001 to 2006. Most technical data are presented for 2006 (or the most recent year available), while financial data typically are averaged over the available period to smooth out the effect of short term fluctuations. Second, cross country comparisons require standardization of the indicators and the analysis to ensure consistency. Therefore, some of the indicators may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the 2000s, Ethiopia’s annual economic growth has averaged 4.8 percent, compared with only 0.5 percent in the previous decade. Notwithstanding this improvement, current annual growth levels still fall short of the sustained 7 percent needed to meet the Millennium Development Goals. Improved structural and stabilization policies generated an estimated 4.2 percent of Ethiopia’s improved per capita growth performance during the 2000s, and improvements in the country’s infrastructure platform over that period contributed up to 0.6 percentage points to growth. This was due almost entirely to the introduction of mobile telephony in Ethiopia. Simulations suggest that if Ethiopia’s infrastructure platform could be improved to the level of the African leader, Mauritius, annual per capita growth rates could increase by 3
this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors.

Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014).<sup>70</sup> Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM.

There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. For example, profiling exercises in Pakistan do not cover all IDPs or areas affected by displacement due to insecurity, and in Afghanistan profiling of IDPs by UNHCR underestimates the scale of the initial displacement as IDPs are only interviewed once displacement sites are accessible, if they are profiled at all (IDMC 2015). Additional challenges include unwillingness of IDPs to participate due to fear of persecution, and mobile populations (IDMC 2008). There may also be political pressures to inflate or reduce numbers.

# **_Population movement tracking systems_**

In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates
3 from the agreement such as political cooperation, enhanced negotiating power and better credibility for the members' general economic reform programs, the possibility of achieving otherwise unattainable economies of scale in production, or dynamic trade gains. These are legitimate objectives which, if achieved, could well more than offset trade distortionary effects. In the present context, however, nothing can be said about them. Second, although there are other possible standards, the counterfactual comparison used in this study is an equivalent degree of liberalization on a nondiscriminatory basis.

# II. Mercosur: Recent Trade Trends

Available data indicate that member countries' trade patterns have experienced major changes since the formation of Mercosur.I For example, Table I provides summary statistics on Mercosur's intra-trade and on exports to destinations such as all OECD countries, NAFTA, or OECD Europe. These data are shown for selected periods from 1979-81 to 1994 in order to help determine when trend changes occurred.2 For the earlier intervals three year periods were used to tabulate data in order to reduce the influence of any annual irregular variations in the trade statistics -- such as those that might accompany significant fluctuations in commodity prices. However, statistics for 1993 and 1994 are shown separately in order to more accurately reflect the recent influence of Mercosur on trade flows.<sup>3</sup> 'This analysis has been restricted by data availability to exploring trade patterns only up to and including 1994. This is sufficient to identify the effects of preferential trade policies, however, for some preferences were included in the Argentina-Brazilian sectoral agreements at the end of the 1980s and widespread preferences were introduced in the transition period for Mercosur starting in June 1991. However, our exercise clearly sheds no light on events since 1994, such as the further progress towards a Common External Tariff or any progress in the non-trade policy aspects of the agreement. These remain on the agenda for further research 2Globally there has been a long-term decline in the relative importance of the Mercosur countries. According to UN COMTRADE data
Beyond the short-term consumption benefits from program-related resource transfers, the predictable nature of such labor demand could affect agricultural outcomes and productivity in two ways. On the one hand, MNREGS could increase investment and intensity of cultivation, allowing farmers to use input levels that are closer to the optimum, or to adopt crop portfolios with higher risk-return profile. On the other hand, assets created through MNREGS works, especially if related to irrigation or other types of land improvement, should increase productivity and reduce production risk directly. Evidence on productionrelated impacts of MNREGS is limited, partly due to data challenges.<sup>10</sup> As a key feature of the program, in contrast to, for example, a straight cash transfer scheme, is the construction of productive infrastructure, this is a central issue for assessing its impact.<sup>11</sup> One category of studies assesses the extent to which availability of the program allows farmers to take on higher levels of production risk. For example, district level Agricultural Input Survey (ACIS) data with a RD framework between phases I and II suggest MNREGS-induced wage increases increased the likelihood of mechanization by 10-20 points in the short term (Bhargava 2014). This could raise the possibility that, in the longer term, program-induced wage increases may prompt a reduction in labor demand. Repeated cross sections based on national NSS data with a RD design suggest that farmers exposed to NREGS make riskier crop choices (Ragunathan and Hari 2014). Panel data from AP point towards a modest MNREGSinduced shift from traditional towards commercial crops such as cotton (Gehrke 2014).

To assess whether higher levels of risk-taking are due to farmers expecting MNREGS to continue in the foreseeable future or a reduction in risk due to program-related works (e.g. in irrigation), data on the amount, type, and quality of the structures constructed under the program are needed. The only study that, to our knowledge, links outcomes to nature and quality of MNREGS works, finds higher that