rafmacalaba/data-use-mentions-extended huggingface dataset:
%|80%|

The administrative data collection approach was simple data entry from official mahalla records in selected PSUs. Each mahalla in Uzbekistan is legally obligated to maintain such records for every person and family residing in that location. These data cover the total population size, demographics, social protection programs, local amenities, and migrant populations. Following the conclusion of the survey data collection fieldwork, the survey data design weights were adjusted on observed population totals in the administration records within the mahallas. These estimates were assumed to be more accurate as they had been updated within three months of the time of the survey fieldwork. Although not identical, there is a strong correlation between the migration descriptive statistics derived from the mahalla records, and those observed in the baseline survey data (as reported in Table 2).

The second stage procedure was conducted using simple random selection of households with equal probability within selected mahallas. A separate stratification level for households that receive social assistance was included, totaling 4 households per mahalla. The final target sample included 20 households per mahalla, 800 of which were social protection recipients by design. The baseline survey included a full consumption and expenditure module using a list/recall approach. The resulting estimates are representative for 12 regions (referred to as provinces in Uzbekistan), 1 autonomous republic, and 1 independent city (Tashkent), crossed with their urban areas (except for the City of Tashkent, which is entirely urban). The baseline survey was conducted entirely on tablet devices (CAPI), enabling validation using cross-referencing, confirmation using geo-coordinates, and other techniques to ensure accuracy. The survey was conducted over the course of a 1.5-month period in May/June 2018. Survey descriptive statistics from the baseline and from the first wave of the panel survey are included in table (3).

6
1.530|1.556|
|**2001**|1.8%|0.586|0.592|1.661|1.696|
|**2002**|1.4%|0.580|0.585|1.522|1.545|
|**2003**|1.6%|0.575|0.580|1.474|1.498|
|**2004**|1.2%|0.564|0.568|1.618|1.638|

<u>Source: Authors' calculations from the PNADs.</u> 40
transport, security, ICT, and research and science). Lockdowns were initially imposed, progressively lifted during the summer of 2020, and then reintroduced by some governments over the course of the pandemic; thus, these restrictions had significant consequences on the ability to go to work. Based on these criteria, we identify essential workers at the three-digit ISCO-08 classification codes (which includes 182 occupations).

# **“Non-teleworkable” Jobs**

The second measure we use is related to the degree to which the tasks of a given occupation can be performed from home. Recent literature on the impact of COVID-19 on labor markets highlights the feasibility of home-based work (telework) as the key to overcome governments’ mobility restrictions and associated income/unemployment risks, and as a way to limit exposure to COVID-19 (Dingel and Neiman, 2020; Garrote Sanchez et al., 2020). In constructing our measure of the “teleworkability” of a job, we follow Dingel and Neiman (2020), who calculate the amenability of different occupations for telework in the United States by using surveys from the Occupational Information Network (O*NET) on work context and generalized work activities. Jobs that are not considered to be amenable to telework arrangements are defined to include those with the following attributes: requiring workers to spend most time outdoors; requiring workers to

> 4 <u>https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020XC0330(03)</u>

6
share of Bank assistance to IBRD countries and, to a lesser extent, IDA countries. Bank assistance for capacity building, in particular, focused mainly on the core public sector, sector administration, and workforce development. More modest commitments were made in support of private sector and demand-side capacity building.

The Bank also attempted to adopt new operational approaches in order to support country ownership and domestic accountability. The launch of PRSPs provided an impetus to try new aid modalities in IDA countries, including a shift to programmatic budget support and multisector investment operations; analytical and advisory activities to develop country-led governance strategies; and efforts to harmonize activities with other donors.<sup>19</sup> In addition, the Bank committed itself increasingly to use—rather than bypass—country systems (World Bank 2003a). The Bank also attempted innovations in its financial and nonfinancial product lines to provide added incentives for institutional change. By the mid-2000s, these efforts produced some tangible results, for example, in public financial management and revenue administration. A 2008 IEG review claims that Bank support in those two areas gained traction (IEG 2008b). So did Bank support for merit-based recruitment and promotion. Outside observers began to take notice of these improvements in the Bank‘s approach. For example, a 2002 global poll of opinion leaders across regions—in particular in Africa—noted that “improvements outweighed setbacks” in the Bank’s efforts to strengthen governance. Certain aspects of Bank support (for instance, civil service reforms and anticorruption efforts) did not produce the desired results. Direct measures, such as the promulgation of anticorruption laws and the establishment of anticorruption commissions, did not reduce the perceived incidence of corruption in countries (IEG 2008b). The global poll showed that efforts to reduce corruption remained one of the Bank‘s least effective efforts.

How to support institution building, particularly in fragile states,<sup>20</sup> was viewed as a major operational challenge. For instance, two separate 2005 reviews
and satisfaction with services is especially low in rural areas. A recent World Bank impact evaluation also found that these factors lead to shortfalls in demand and under-utilization of public health services.

When cases of COVID-19 were first confirmed in Tajikistan in April 2020, there were few reliable tools to quickly convey urgent health information to the public. Survey data collected during that time show that television emerged as the primary source of official announcements, while relatively small numbers of people also got information from newspapers, the internet, and social media. A somewhat larger number of people received their information in-person from health care providers and other officials; however, these interactions were still limited, especially in areas with unreliable transportation and other infrastructure. The challenges of communication and service delivery were further complicated in an environment where in-person contact was unsafe due to the risk of spreading infection.

**Figure 1: Daily number of confirmed cases**

<!-- Start of picture text --> 250<br>May 20, 2020: Message<br>sending begins<br>200<br>150<br>100<br>50<br>0<br><!-- End of picture text --> _Source: Our World in Data_ To address the difficulty of quickly disseminating key public health messages, the World Bank launched the initial design phase of the Mobile Engage project in March 2020, with financial support from a Trust Fund financed by the Republic of Korea.<sup>6</sup> The project focused on providing crucial public health information about reducing the risks posed by COVID-19 via SMS to the general population.<sup>7</sup> The project received official endorsement from the Ministry of Health and Social Protection of Tajikistan in April 2020. Messages were first sent in May 2020 and managed in close

> 6 <u>https://www.worldbank.org/en/programs/korea-trust-fund</u>

> 7 In parallel, the Mobile Engage project
# **Appendix 1: Constructing the measure of access to finance**

We use the World Bank Enterprise Surveys dataset (www.enterprisesurveys.org) assembled with a module of identical questions included in all questionnaires. The common framework of the questionnaire enables crosscountry analyses using variables specified in the core module.

A complication in constructing the measure of access stems from changes in the core survey modules made for surveys administered after 2005. That is, the variables required to construct a measure of access are defined differently in the old (2002-2005) and new (2006-2010) core modules..

From the old surveys, we consider the following questions:

- “Do you have an overdraft facility or line of credit?”: Yes/No

- “For the most recent loan or overdraft”:

- When was this financing approved (year)?

- Did the financing require collateral or a deposit?

- If yes, what share of collateral was:

- Land and buildings?

- Machinery?

- Intangible assets (accounts receivable, inventory)?

- Personal assets of owner/manager (e.g. house)?

- What was the approximate value of collateral required as a percentage of the loan value?

- What is the loan's approximate annual cost/ rate of interest?

- What is the duration (term) of the loan?

From the new surveys, we consider the following questions:

- “At this time, does this establishment have an overdraft facility?”: Yes/No

- “At this time, does this establishment have a line of credit or loan from a financial institution?”: Yes/No

31
shows a clear difference in firm size distribution between the two datasets. For the case of Kenya, a comparison of the ES (for the years 2007 and 2013) against the 2010 Census of Business Establishments (CBE) reveals that the size distributions of firms in the two datasets track each other quite closely. This can be explained by the fact that, unlike in the other countries, the CBE is actually being used to create the sampling frame in Kenya.<sup>20</sup> Even though there is no unique mechanism through which biases in the size distribution could be conveying biases in the distribution of distortions, one would be, in principle, more reassured about Kenya’s distribution being close to the Census-based one, assuming the latter is the best representation of the true distribution of firm sizes. We will see below, however, that one also needs to worry about the representativeness of the distribution of sectoral value added shares.

# **7.2 Extent and Cost of Misallocation: Survey vs Census**

We turn now to evaluating the degree and costs of misallocation as measured from the ES. The goal is to see whether the divergence in the size distribution in the case of Ghana, and its similarity in the case of Kenya, are informative about differences or similarities in the extent of misallocation in these economies when compared to the calculations based on the Census.<sup>21</sup> There are a number of challenges involved in making the analysis of misallocation in the ES comparable to that in the Census. First, the surveys for the four countries under study are not built to ensure representativeness of in terms of disaggregated sectoral coverage. As mentioned above, there is stratification across two broad categories, manufacturing and services, in the case of Ethiopia, while representativeness is captured only at the two digit level in Ghana and Kenya. Adequate sectoral representativeness is important for our calculation because aggregate sufficient statistics of misallocation and the aggregate counterfactual gains in productivity are all constructed based on
Policy Research Working Paper 10206

# **Abstract**

This paper uses labor force survey data for 1995–2020 to analyze the dynamics of job tenure in seven transition economies of Europe and a comparator country (Türkiye). The country-specific age-period-cohort decomposition demonstrates that, except in Albania, the job tenure of the cohort of workers entering the labor market in the 2000s is four to nine years shorter than that of workers who started working in the 1970s. This difference is at least twice as large as the difference in job tenure observed among workers from the same cohorts in European Union countries. These trends in tenure persist after accounting for changes in cohort composition, but they are significantly attenuated by controlling for differences in individual worker characteristics. These results suggest that the evolution of tenure in the transition economies of Europe is still driven mainly by the transition-induced structural change processes in the labor market.

This paper is a product of 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 mbussolo@worldbank.org, mlokshin@worldbank.org, and itorre@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 of the Executive Directors of the World Bank or the governments they represent._ Produced
Thus, the total effects of tourism on income distribution and poverty reduction depend on more than just the level of spending by tourists on various commodities and services, and who receives the direct employment and incomes from these purchases. The overall impact of tourism also depends on the size of the multiplier effects on output of other sectors, and the distribution of the revenues from increased production to various factors (labor and capital) and ultimately to household groups (poor and non-poor). These multiplier effects are particularly important for spreading the benefits of Panama’s tourism industry to the poor, since many of the poor do not have direct contact with tourists, themselves.

The multiplier effects of tourism revenues (and growth in outputs of other sectors) can be estimated using a semi-input-output (SIO) model of Panama’s economy. In the SIO model, output of some sectors, typically those producing tradable goods, is assumed to be fixed (completely inelastic), and does not expand in response to increases in demand. For these products, increased demand results in increased net imports. For elastically supplied products, however, increased demand is assumed to induce increases in output.<sup>16</sup> The data base for the model is a Social Accounting Matrix (SAM) for Panama for 2003 which describes the input-output structure of production, the distribution of earnings of labor and capital to various household groups, and patterns of spending. In order to enable the simulation of distributional effects of policy, the SAM includes nine productive factors (four types of labor, agricultural land, and four types of capital) along with eight household groups (urban poor and non-poor, rural poor and non-poor for each of four regions Panama City and Canal Zone, Bocas del Toro, Chiriqui and Other Panama) defined using household survey data (Table 5).

**Table 5: Panama: Size and Expenditures of Major Household Groups, 2005**

||**Population**<br>**('
18 cities or a policy commitment by the overlying state governments to support these smaller jurisdictions. 9 The Nepal data are drawn from a small sample of eight rural district panchayats. The sum of grants and shared (land-based) taxes constituted over 90 percent of the total revenues in these rural districts that represent both terai and hill areas. Obviously, very little reliance is placed on own-source revenue mobilization in these jurisdictions.

The Pakistan data are shown for both rural local governments (districts) and provinces. The district-level data emphasize the fact that under a federal system each individual province determines its own intergovernmental relations. Districts in the two less developed provinces--Buluchistan and the North West Frontier Province (NWFP)--rely considerably more on the provincial level of government than is the case in the more highly developed provinces. Similar differences are observed when the importance to provinces of federal tax shares and grants from the federal to provincial levels of government are considered. Again, subnational governments in the less developed areas of the country are not required to mobilize as much of their own revenues as are higher income and more urbanized regions.

Districts in Bangladesh are shown to rely upon the shared property transfer tax to provide them with nearly one-half of total revenues with grants providing an additional one-third. Reliance upon grants, 9The World Bank, "Financing Urban Services in India," South Asia Urban and Water Supply Department (mimeo), 1984.
18

# **(f) Survey of former employees**

In order to know what happened to the former employees of RFFSA and to check if the resources allocated to training were effective in maintaining the standard of living, RFFSA's management commissioned a detailed monitoring in each region, based on field interviews. RFFSA retained the Rio de Janeiro Catholic University (PUC) to conduct these interviews. This survey began in January 1998, in Sao Paulo. Eventually, it covered 5,334 former employees who left the company between January 1995 and October 1997 and was distributed over 600 municipalities. The reply percentage was over 77% (including 90% of men) which is high but not complete because 1,217 workers moved without a trace. Only seven percent of the workers covered by the sample refused to cooperate The forst survey showed that the average worker interviewed is 43 years old. The education level is quite heterogeneous and higher than the Brazilian average, with only 1 % "analphabet" and 4% with a post-graduate degree. The majority (22%) had a high-school diploma. Almost half (2,301) work on their own and 20% have opened their own business. Only 18% are employees and 4% are civil servants. About 13 % are employed illegally. Most found the training good but irrelevant to their new job. About 44% of these employees have been in their new job for an average of 13.2 months. Overall, they all earn less than when they were working for the railways.

A more detailed survey conducted among 675 formers employee in September 1998 was also quite revealing. Only 10% where unemployed but they count most of the older workers and/or the less educated. Also the salary structure had changed quite a bit. In particular, the survey shows that while 53% of the workers are earning less than when they were at RFFSA, 27 % and making a better living. In general, the dispersion of wages increased for that
As Särndal and Lundström (2015) argued, the calibration approach can easily leverage the wealth of information available on non-response; if the auxiliary variables are explicative of the non-response, the calibration estimator reduces the no-response bias. In this way, the estimates jointly smooth the non-response bias and calibrate the known population totals. This approach has three advantages for the method often adopted for producing survey estimates, which is based on performing preliminary adjustments for non-response based on no-response propensity scores and which produces weights defined at the individual level, implying that people in the same household may have different sampling weights.

The first advantage is that the sampling variance estimation is better founded and more transparent. Calibration estimators behave asymptotically, as they converge in probability to the regression estimator. They produce robust inferences with sound statistical properties for both the sampling design and the statistical model.

The s econd benefit is that fewer adjustment steps are needed, making the process flow for the estimation more straightforward. Särndal and Lundström (2005, pg. 88) note that if the information on non-response is available only at the sample level, the calibration approach can be carried out in two steps, where the first step calibrates on the known totals at the selected sample level, which includes both respondents and non-respondents. The two-step approach improves the accuracy of the estimates when the reasons for attrition are different from the reasons for sampling under-coverage. Thus, two calibrations are implemented: the first calibration adjusts the base weights for panel attrition and the second calibration uses the adjusted weights to produce the final weights to address the under-coverage.

Finally, the third advantage is that the calibration method produces estimates that are defined at the household level. That means all individuals in the same household have the same final weight, which significantly facilitates the consistency of estimates with variables defined at the individual level and those constructed with variables at the household level. ′ = �X , ... , X , ... , X � from administrative data, the latest census, or demographic tt
the informal sector so that they remain under the radar and avoid compliance with such oppressive laws. Furthermore, the provision of childcare may play an important role. If such provisions are not available, women may opt for more flexible arrangements in the informal sector instead of the more time intensive demands of starting a formal business. Evidence has shown that women find the informal sector attractive as it offers an easy balance between market and homecare activities (Maloney, 2004). Thus, the overall impact of legal discrimination may be a greater presence of women in the informal sector. Of course, many of the factors described above could lead women to abandon economic activities altogether. Thus, a negative relationship between informality and legal discrimination is not certain and is in need of empirical verification.

The informality that we analyze in the current study is of a particular type. The Enterprise Surveys only consider formal (i.e., registered) firms and, thus, when discussing informality in this context, we consider only those informal firms that eventually managed to become formal. In relation to the entire informal economy, these are likely to be a subset of high-performance informal firms; however, the question of whether they perform at the same level as their counterparts who were formal from the outset is an empirical one. Indeed, the relationship between whether or not a firm started informally and the performance of the business at the time of the survey is under-explored. Ayyagari et al. (2020) find that a firm’s size when it begins operations is significant in explaining size and growth over its lifecycle, but do not make any statements whether formal status of the firm at birth matters. There are a number of ways the formal-informal status of firms at birth can have subsequent effects on performance. For one, the rare firms that transitioned from the informal to the formal sector may be extremely productive to have made that transition. And, thus, they may exhibit greater performance after formalizing (Williams et al., 2017; Williams