rafmacalaba/data-use-mentions-extended huggingface dataset:
values of the size of the informal sector, as a percentage of GDP.

The VAT evasion rate is calculated as a ratio to _official_ GDP. Therefore, given that the VAT evasion rate has been used to pin down the overall scale and the distance between the ranks of the informal sector, the estimated values of the informal sector reported in Table 1 are also given as a percentage of _official_ GDP. The question remains whether official GDP includes informal production. National account authorities in Latin America are aware of the large presence and order of magnitude of the informal sector in their respective countries. For this reason they rely not only on income reports to estimate GDP but, most importantly, on sampling and surveys of production and prices by economic sectors. Therefore, although actual GDP may be measured with error, the direction of the bias is uncertain. At any rate, official GDP does not ignore the contribution of the informal sector.

# **The Relation Between the Informal Sector, Public Infrastructure, and Economic Growth**

The theoretical model presented in section III links the informal sector with the provision of public services and the rate of economic growth. The model predicts that the relative size of the informal sector is negatively correlated with both the availability of public services and the economy's growth rate (except when the increase in the informal sector is due to a small rise in the tax rate from a sufficiently small level or a drop in enforcement strength from a high and costly level. Nonetheless, the case of suboptimally low statutory tax rates or excessive enforcement strength hardly applies to any Latin American country).

-27-
requirements and competition with the informal sector. Within-firm labor productivity challenges also reflect low skills and capacity—including low technology adoption—among both firms and workers.

These findings suggest that financial and business environment reforms are needed to unlock the private sector’s role in driving jobs and economic transformation. To improve access to finance, Zambia could consider dismantling arrears, scaling up and refining credit guarantee schemes for private sector lending, improving financial infrastructure (e.g., credit information systems), and working with financial intermediaries to diversify product offerings. Regulatory streamlining—including for trade and foreign direct investment—would help decrease competitive distortions from competition with the informal sector and increase exports and FDI, which are critical channels for productivity improvement. Energy sector reforms such as improved systems planning would help to increase the quality of and access to electricity for firms and citizens alike. At the firm level, programs to enhance technology transfer linkages between anchor firms and their suppliers and buyers could help Zambian firms upgrade their capacity and improve productivity, as could programs to directly promote technology use by firms. At the same time, investments in water management and other climate change mitigation and adaptation measures will be critical to build resilience and reduce volatility.

Further research priorities related to firm productivity and wages in Zambia could include more detailed analysis of how specific policy issues, access to finance, infrastructure, and capability gaps interact with firm productivity (e.g., the impact of FDI on firm productivity within Zambia) and pay as well as analysis of wage dynamics and inequality within firms across individual workers. The CIT and PAYE data utilized in this paper could also be used to conduct research related to revenue mobilization and tax compliance.

25
# **A Cautionary Tale: An Experiment on The Stability of Business Environment Perceptions in a Firm Survey**

## **1. Introduction**

The private sector is a fundamental engine of economic growth in an economy. The ability to check the pulse of the private sector is crucial for policy makers to formulate evidence-based recommendations. However, there is a dearth of information on firms across the developing world, thereby generating the need for business surveys such as the World Bank Enterprise Surveys. Questions in the surveys generally come in two forms. The first is what is defined as experience-based questions. Here the respondent, typically the manager or owner of the firm, provides information regarding a specific event, over a defined time frame, on a particular element of the business environment. For instance, “ _Over last complete fiscal year, did this establishment experience power outages?_ ” The second type of question is perception-based. Here the respondent provides a rating, typically using a Likert scale of responses, of a business environment element which is more holistic in nature. For instance, “ _To what degree is electricity an obstacle to the current operations of this establishment?_ ” The choices, consistent across all WBES, are: “no obstacle,” “minor obstacle,” “moderate obstacle,” “major obstacle,” and “very severe obstacle.” Both types of questions address the same element of the business environment, electricity in the example above, but the experiencebased question narrows it down to a specific component of an element of the business environment -usually a key component such as power outages- whereas the perception-based question encompasses all dimensions of that element of the business environment. It is because of this all-encompassing generality that perceptions-based questions may lead to biases or misinterpretations. The aim of this study is to explore the stability of such indicators by evaluating whether the ordering of perception-based questions matters. In this study, we analyze data from a unique experiment conducted in the República Bolivariana de Venezuela in 2006
groups – the lowest wealth quartile, indigenous students, students whose mothers have secondary education or lower, and students without internet access - have less access to remote learning technologies, and are less likely to be doing schoolwork. For example, 22 percent of students without internet access did no schoolwork in the past day, compared to 9 percent of those with internet and a computer or tablet. Nevertheless, while these differences exist, the majority of students in the lowest wealth quartile and of those with no internet access are still engaging in remote learning.

In addition to detailed information on remote learning and time use, our surveys directly ask students questions on mental health status, and on how the COVID-19 pandemic is most affecting them. Most students had spent zero hours outside their house in the past day. The majority identify disruptions in schooling as the biggest issue they face because of COVID-19, with feelings of social isolation the next biggest concern. Despite this, more than two-thirds say they were happy most or all of the time during the past month. While the majority appear to be coping, 16 percent have mental health scores that lie below a threshold indicative of major depression. This suggest scope for psychosocial support to supplement remote learning efforts.

The information provided here is, to date, unique in providing detailed time use information and reports directly from students during the COVID-19 pandemic. Less detailed information in other developing countries is being collected from parents or other household adults as part of rapid response household surveys. An early example is provided by Le Nestour et al. (2020) for Senegal, who provide a more pessimistic view of remote learning there: most students were either doing no educational activities, studying alone, or doing exercises given by parents; with fewer than 5 percent doing exercises given by teachers and less than one percent doing online courses. Ecuador therefore provides an example of much more engaged remote learning.

The remainder of the paper is structured as follows: Section 2 provides details of the context and survey methodology;
br>by GDP (average, 1991-<br>it HS categories.|5).<br>1995).|

_Source: GAIT, TPR; WTO, TPR; World Bank 1998._

# 3. Trade Remedies

Trade remedies are defined to include anti-dumping, countervailing and safeguard actions. In principle such actions are consistent with GATT provisions. The legal basis and procedures for the imposition of trade remedies in each instance are different as are the remedies, which usually do not involve quantitative restrictions but changes in duties and charges to address the problem as appropriate in each case. In the case of antidumping and countervailing duties, remedies are intended to correct for distortions that occur when exporters are obtaining subsidies and engaging in discriminatory pricing practices which result in injury to domestic producers. In the case of safeguards, the issue is simply injury to domestic producers - even if no unfair trade practices are involved9 The evidence regarding the frequency with which developing countries have taken trade remedy actions is presented in Table 11. For antidumping measures, the WTO data base contains information from which tariff line frequencies were calculated. As it has been argued that the mere initiation of investigations regarding antidumping action tends to have a restraining effect on imports - irrespective of

> 9Also, countervailing and anti-dumping actions are directed against the imports from only one country and not all tradealthough they may thereby create disincentives to exporters of other countries as well. For these reasons, it **is considered** appropriate to keep the total frequency ratios for each group of measures separate (unlike the OECD study which adds them up for each country; see OECD, 1997, Table 5.1 ).

27
as it gives practitioners a sense of how much they could reasonably expect to gain from double sampling. It also facilitates comparisons between the sample direct estimator and the optimal single sampling estimator.

Ideally, all the parameter values should come from a single survey. However, we are unable to do so due to the lack of data. In particular, the information on survey costs is typically unavailable to the public. Therefore, we collect empirical values of _c_ , _τ_ , , _wc_ , and _wh_ from various sources. For each of these parameters, we specify low, mid, and high values. The low and high values are close to the minimum and maximum observed in our data sources. The mid value is either the arithmetic or geometric mean of minimum and maximum. The parameter values are presented in Table 1. The details of the data sources and assumptions are provided in 22
the early stage of _doi moi_ reform, more males migrated inter-provincially, but the gender-based trend reversed in recent years as more females moved across provinces

> 2 Since the first Law on Foreign Direct Investment took effect in 1987 and was subsequently amended in 1990, 1992, 1997 and 2000 (Van Arkadie and Mallon, 2003), FDI has played an important role in Vietnam’s economy. For the year 2009, foreign-owned firms accounted for 18.7 percent of Vietnam’s Gross Domestic Product (GDP) and 43.2 percent of industrial output (GSO, 2010).

> 3 During the period 1986-2011, Vietnam experienced an average annual growth rate of 6.9 percent (the World Development Indicator, the World Bank).

> 4 The urban share of the population increased from 23.7 percent in 1999 to 29.6 percent in 2009 (GSO, 2011).

2
enlarged to increase the precision and make it representative at the Tehsil level (a level below the district). The total number of surveyed households is 30,932 in 2003 and 91,280 in 2007-08. For the empirical models of this paper, the analysis uses pooled cross-sectional data comprised of girls aged 12-19 selected based on the criteria described above.

The surveys used to construct covariates for the school-level analysis are again used for the household-level analysis. This includes the PSLM, which relies on a similar sampling strategy as the MICS. Other data come from the PIHS, the National Education Census, administrative data of other programs under the PESRP, and the annual Punjab public school census collected in 2003.

Given the targeting of the program to low-literacy districts, stipend and non-stipend districts are likely to differ in aspects other than the treatment and a baseline comparison confirms this. Since literacy rates are likely correlated with other underlying socio-economic characteristics, such factors may, in turn, be associated with different education attainment and divergent trajectories in human capital accumulation between households in stipend and nonstipend districts. Indeed, as shown in Table 3, at the community level, households in stipend districts are more likely to be below the poverty line, to be located in a rural village, and to spend a little less on education. The stipend districts also have less access to public transport and to schools of different levels (defined as a distance of two kilometers or travel time of 15 minutes) as well as fewer private middle schools relative to public schools. At the school level, schools in stipend districts tend to have worse school facilities, but the differences are very small, except for electricity. On the other hand, stipend schools exhibit more favorable student-teacher ratios. Finally, at the household level, households in stipend districts appear to have more dependents and be headed by individuals with approximately 1.6 fewer years of education. They are also less likely to have dwellings connected to gas and electrical services, and to have access to family planning
<!-- Start of picture text --> Reviewed in the study 133 100% 734 100% 1,297 100%<br>With each type of disability question:<br>- Washington Group Short Set (WGSS) 32 24% 4A 6% 61 5%<br>- Other functional difficulty questions 46 35% 67 9% 90 TM%<br>- Activity of Daily Living (ADL) questions 15 11% 24 3% 36 3%<br>- Broad activity limitation questions 36 27% 53 7TM% 71 5%<br>- General disability questions 50 38% 81 11% 113 9%<br>- Other disability questions 43 32% 54 7% 75 6%<br>With any disability question 100 75% 231 31% 350 271%<br>With functional difficulty questions (WGSS and/or other) 66 50% 111 15% 156 12%<br><!-- End of picture text -->

|Reviewedinthe study|133|100%|734|100%|1,297|100%|
|---|---|---|---|---|---|---|
|Witheachtypeofdisability question:<br>|||||||
|-WashingtonGroupShort Set(WGSS)|32|24%|4A|6%|61|5%|
|- Otherfunctional difficulty questions<br>|46<br>|35%<br>|67<br>|9%<br>|90<br>|TM%<br>|
|-Activity ofDailyLiving(ADL)questions|15|11%|24|3%|36|3%|
|-Broadactivitylimitationquestions|36|27%|53|7TM%|71|5%|
|- General disabilityquestions|50|38%|81|11%|113
The HS is an international nomenclature developed by the World Customs Organization (WCO) for classifying traded goods. It provides a standardized way of categorizing products for customs, trade, and tariff purposes.

The ISIC – HS concordances serve as a bridge between these two classification systems, allowing us to link economic activities (ISIC) from the Sakernas data to specific products or goods (HS) in the UN COMTRADE data.

# _World Development Indicators Data_

The World Development Indicators (WDI) is a comprehensive dataset maintained by the World Bank that provides a wide range of economic, social, and environmental data for countries around the world. The dataset covers a variety of topics and indicators, offering insights into the development status and progress of countries over time. We use data on foreign GDPs from all commercial partners to Indonesia to build our Bartik instrument.

# _FRED-PCE Deflator_

The Federal Reserve Economic Data (FRED) provides access to a wide range of economic data, including the Personal Consumption Expenditures (PCE) deflator, which is considered a more comprehensive measure of inflation by some economists due to its dynamic basket of goods and services. In this study, we use the PCE deflator to express the value of exports and wages in real terms.

21
<!-- Start of picture text --> Female-headed<br>Male-headed<br>Refugee - Current<br>Refugee - Before disp<br>Host community<br>0 10 20 30 40 50 60 70 80 90 100<br>Agriculture Self Employment Paid Employment Aid Remittances<br><!-- End of picture text --> Source: Author’s calculation using Skills Profile Survey (2017), Ethiopia Figure 4: Source of livelihood by gender of the household head and refugee status To examine the factors influencing the gender differences in livelihood activities among the four refugee hosting regions, we undertook an in-depth empirical study. We therefore focus on the empirical work to estimate the gender gaps among refugees and hosts, understand what drives the gender gaps, as well as compare refugees and hosts across regions. Thus, the gendered analysis will help to determine the various needs and capacities of refugee women and men.

# 4 Empirical Strategy

In our empirical analysis, we use two different models to examine the gender differences in livelihoods among the forcibly displaced and host communities in Ethiopia.

We begin by examining the gender differences in the probability of being in employment (formal or informal). A probit model is used where the dependent variable takes a value of one if an individual is in employment and zero otherwise. 1 2 )--------------------------------------------------- (1) ii dd ii ii PP = FF(αα + ββ XX + ββ ZZ The model is estimated separately for female- and male-headed households as well as female and male individuals to see the differences at both household and individual levels. Where i is the probability of an individual/household in year being in employment (regular and casual), are camp fixed effects that capture
amenities. Wineries are becoming a complementary touristic attraction, and more generally a staple export industry, making headway into international markets with the flagship Tannat reds. Software development and other ICT-related activities have become an increasingly important engine of export growth in recent years, reaching US$980 million in 2022.

**Figure 2: Uruguay merchandise export composition**

<!-- Start of picture text --> 9%<br>Other<br>12%<br><!-- End of picture text --> <!-- Start of picture text --> Livestock<br>31%<br><!-- End of picture text --> <!-- Start of picture text --> Industrial<br>Manufacturing<br>11%<br>Crop and<br>Paper and Animal and<br>Wood<br>Vegetable Oils<br>12%<br>25%<br><!-- End of picture text --> Source: Based on Uruguay XXI. Notes: Average from 2019-2022.

6
Delay Child Marriage in Rural Ethiopia.” _International Perspectives on Sexual and Reproductive Health_ 35(1):6–14.

- Eswaran, Mukesh and Nisha Malhotra. 2011. “Domestic violence and women’s autonomy in developing countries: theory and evidence.” _Canadian Journal of Economics/Revue canadienne d’ ́economique_ 44(4):1222–1263.

- Farmer, Amy and Jill Tiefenthaler. 1997. “An economic analysis of domestic violence.” _Review of Social Economy_ 55(3):337–358.

- Garc ́ıa-Ramos, Aixa. 2021. “Divorce laws and intimate partner violence: Evidence from Mexico.” _Journal of Development Economics_ 150:102623.

- **URL:** _https://www.sciencedirect.com/science/article/pii/S030438782030198X_

- Gersovitz, Mark, Hanan G. Jacoby, F. Seri Dedy and A. Goz ́e Tap ́e. 1998. “The Balance of Self-Reported Heterosexual Activity in KAP Surveys and the AIDS Epidemic in Africa.” _Journal of the American Statistical Association_ 93(443):875–883.

- **URL:** _https://doi.org/10.1080/01621459.1998.10473744_

- Gibbs, Andrew, Laura Washington, Nada Abdelatif, Esnat Chirwa, Samantha Willan, Nwabisa Shai, Yandisa Sikweyiya, Smanga Mkhwanazi, Nolwazi Ntini and Rachel Jewkes. 2020. “Stepping Stones and Creating Futures Intervention to Prevent Intimate Partner Violence Among Young People: Cluster Randomized Controlled Trial.” _Journal of Adolescent Health_ 66(3):323–335. **URL:** _https://www.sciencedirect.com/science/article/pii/S1054139X19304847_

- Goerg, Sebastian J. 2015. “Goal setting and worker motivation.” _IZA World of Labor_ 178:1–10. **URL:** _10.15185/izawol.178_

Grassroot Soccer. 2013. Zinduka Coach’s Guide. Technical report
sup>nd</sup>year|185|205|320|355|
|3<sup>rd</sup>year|195|225|335|390|
|High school|||||
|1<sup>st</sup>year|||510|585|
|2<sup>nd</sup>year|||545|625|
|3<sup>rd</sup>year|||580|660|
|Max HH amount without high-schooler|550||950||
|Max HH amount with high-schooler|||1635||

Note: Amounts in nominal pesos. The peso-to-dollar exchange rate was exchange rate was roughly 8 in 1997 and 11 in 2003. Source: www.prospera.gob.mx.

How might PROSPERA affect enrollment, school attended and achievement? In particular, conditional transfers should increase enrollment through both a price and an income effect. The price effect occurs because the subsidy reduces the shadow wage (or relative value) of children’s time in activities other than school. There is also an income effect, because the subsidy increases a family’s total potential income. Thus, both the price and income effect of the transfers work to increase a child’s enrollment in school.

In addition to increasing the probability of enrolling in school, the program might also change the school in which a child enrolls. Part of the transfers might be spent on costs necessary to attend a better school, for instance in paying additional transportation costs for a school that is located further away from residence. The extent to which this is possible, of course, depends on school options within a reasonable distance. For example, if there is only one high school within 50 kilometers, it is unlikely that the transfers would affect the school attended. In this paper, we 5
gap (e.g. Perman and Tavera 2005). These variables are calculated as the difference between observed values and potential GDP and the ‘natural rate of employment’ (mirroring the more commonly used natural rate of unemployment) respectively. These two unobserved measures have to be estimated. One common method is using the Hodrick and Prescott filter which decomposes integrated series (such as GDP and employment) into a stochastic trend and a cyclical component (Hodrick and Prescott 1980, 1997). This method is applied here, using a smoothing parameter of 1600 which is recommended for quarterly data (Rven and Uhlig 2002). The output gap is then calculated by subtracting the logged GDP trend from the logged GDP values. Similarly, the employment gap is calculated – for total employment and agricultural/non-agricultural employment where data are available – by subtracting the estimated employment trend from the observed employment data. For illustration, Figures 1 and 2 plot the output and employment gaps for Hong Kong SAR, China and Malaysia respectively.

4