rafmacalaba/datause-encoder-data huggingface dataset:
# _Interviewer characteristics_

The mean values for interviewer characteristics are provided in Table 2. These characteristics are used as covariates in the analytical approach, which is described in the section below.

**Table 2: Interviewer characteristics**<sup>**6**</sup>

|**Interviewer characteristic**|**Value**|
|---|---|
|Female (%)|40|
|Age (mean years)|40.2|
|Lives or has lived in the survey state (Kassala) (%)|50|
|Attended university or higher (%)|77.8|
|Works with CBS (%)|82.4|
|Conducted 10 or more CAPI surveys previously (%)|61.1|
|Self-rated skill with tablets or smartphones before survey (1 – 10) (mean)|9.3|
|“On a scale of 1 to 10, how much do you generally trust other people?” (mean)|6.8|
|“On a scale of 1 to 10, how much do you think other people generally trust you?”|7.6|
|(mean)||

# **3. Analytical approach**

This research seeks to identify interviewer effects and their causes through four different specifications of a model that measures interviewer ICC. Following Brunton-Smith _et al_ . (2016), we use two-level mixed effects linear regression models with respondents nested in interviewers. We deploy four different versions of this model. The first specification simply measures the interviewer ICC for each variable. Specifications II and III aim to control for covariates including interviewer area clusters and the gender of interviewers and respondents. A fourth specification includes interviewer characteristics in an attempt to identify their contribution to interviewer-related measurement error. Note that for each specification, we are only interested in the ICCs that are obtained post-estimation and not the coefficients themselves.

The first specification simply identifies the ICC without controlling for possible covariates:

# Specification I:    yij = β+ uj + εij

where _y_ is each of the ten dependent variables in table 1; _i_ is the households interviewed by interviewer _j_ , where j runs from 1 through 20; _β_ is the is the intercept, uj is the level 2 (interviewers) random intercept; and εij is the level 1 error term. For this model, the ICC is calculated using the formula below.

> 6 Except for female where all 20 interviewers responded, all responses are based on n = 18 except age and worked with CBS, which only had 17 respondents.

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8 secondary - informal - activities, a high proportion of the labor force has informal work activities as main occupation (see **Table 3** ). Another important form of possible informal activities can be self-employment. Although one could have expected to see self-employment develop rapidly during the transition, available data indicate that self-employment remains very limited in Ukraine. About _5_ percent of working men (and 3 percent for women) report self-employment as their main occupation and a very small proportion of the labor force combine self-employment with other types of occupation.

**ZablQ** 3

Labor **Force Participation Rates Employment Rates** in **Formal** e Informal **Sectors** - **1995 -**

||MEN|WOMEN|
|---|---|---|
|Labor force participation rate for population aged 15+|.79|.71|
|Total Employment rate (Population aged 15+)|.76|.70|
|Employment rate in the formal sector|.51|.37|
|% of those working in the formal sector:|||
|- also working on land plots|.66|.67|
|- also working as self- employed|.05|.04|
|Note:|||

1. In the calculation of employment rates, unemployed are excluded.

2. People working exclusively on land plots or as self-employed are assumed to belong to the informal sector.

Source: 1995 Household Survey The fact that a significant proportion of the labor force allocates time - sometimes exclusively - to occupations that can be qualified as informal and thus not subject to tax enforcement leads to a significant reduction in the number of contributors to the public social security system. **Chart** 2 shows total male and female activity rates as well as rates calculated on the basis of participation to formal employment5 . Employment rates in the formal economy are found to be substantially lower than total employment rates for both men and women. The discrepancy between these two rates is particularly strong for women. Overall, while 76 percent of men and 70 percent of women aged 15+ report working activities, only 51 percent of men and 37 percent of women are engaged in formal activities (though not exclusively). Thus, _one third of men and approximately one half of women in the labor force are likely to avoid any form of taxation of their labor income._ It is also worth noticing that most of the increased work activity observed in recent years both at young and old ages is taking place in the informal sector (see **Chart 1).** Individuals working exclusively on land plots or as self-employed are considered as working in the infornal economy.
|Table A2. <br>|Price variables<br>|
|---|---|
|De<br>|Price ofaggregate output in region r<br>|
|p=<br>|Price ofenergy<br>|
|p=?|Price ofenergydemand in region r|
|w,.|Wage rate in region r|
|v,|Return to capital in region r|
|qr|Rent to natural resources in region r|
|pA<br>|Price ofArmington good in region r<br>|
|piv|Welfare price|
|pr|Priceofemissionspermits|

|Table A3|. Additional variables|
|---|---|
|CO2,.|CO, emissions in regions r|
|RA,|Income level ofrepresentative agent in region r|
|U,.|CDM incentive subsidy|
|T,.|CDM emissions tax|
|T5,|ValueofCDMpurchases|

|Table A4.|Cost shares|
|---|---|
|ie<br>a<br>|Benchmark cost share ofenergy in aggregate output in region r<br>Benchmark share ofcapital in value-added composite in region r<br>|
|gk<br>el<br>|Benchmark share ofspecific resource in energy production in region r<br>Benchmark share ofoutputfrom region s in consumption composite of region r<br>|
|plw|Benchmarkshareofleisureinwelfarecompositeinregionr|

# Table A5. Endowments

|L,<br>|Laborendowment in region r<br>|
|---|---|
|Is,|Benchmark leisuredemand in region r|
|l.<br>K,.<br>|Benchmark labor demand in region r<br>Capital endowment in region r<br>|
|fi.|Benchmarkcapitaldemandintheenergynestinregionr|
|k,.|<br>Benchmarkcapitaldemandintheoutputnestinregionr|
|Q,.|<br>Resourceendowment in region r|
|co,<br>|Emissions target<br>|
|ee<br>|Benchmark bilateral trade flows<br>|
|ed,|Benchmarkenergydemand in region r|
|Table A|6. Elasticities|
|o|Substitution between energy inputsand the capital-labor composite|
|n|Substitution between capital and resources in the energy nest|
|Y|Substitutionbetweentheimportaggregateandthedomesticinput|

<!-- Start of picture text --> Table A6. Elasticities<br>o Substitution between energy inputs and the capital-labor composite<br>n Substitution between capital and resources in the energy nest<br>Y Substitution between the import aggregate and the domestic input<br><!-- End of picture text -->
**Figure 3. Microfinance Products offered by MENA MFIs**

<!-- Start of picture text --> Region Total<br>Egypt<br>Morocco<br>Lebanon Microenterprise<br>Jordan Islamic finance<br>Iraq Consumer<br>Palestine<br>Housing<br>Syria<br>Mortgage<br>Tunisia<br>Yemen<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: Sanabel 2010 (2008 data)

**Microsavings are offered on a very limited scale in the region** , and are only found in West Bank & Gaza, Syria, and Yemen and only offered by a few institutions.  Al Amal Microfinance „Bank‟ in Yemen, for example, offers sight deposits, child savings accounts, and also time deposits, as well as Shariah-compliant insurance on loans (Takaful). Micro insurance for health and education (beyond standard death and disability protection offered by many MFIs on microloans) is being introduced by leading microfinance NGOs in Egypt and service companies, as service agents for insurance companies (Allianz and Alico).

# **Trade credit (credit provided by other firms) is used by many micro and small enterprises**

**in the MENA region.** A significant proportion of micro and small enterprises interviewed for IFC microfinance country reports in 2006-2008 reported accessing supplier credit, and most paid little or no interest charges. Half of micro and small enterprises surveyed for the IFC in Yemen (205 out of 405), 39.5 percent of those surveyed by Syria, 40.7 percent of those interviewed in Algeria, and 40.7 percent of those in Lebanon, used some form of supplier credit to acquire supplies, whether as an advance with balance upon receipt, or making a delayed cash payment after delivery.<sup>10</sup>

**Islamic financing instruments comprise only a small fraction of microfinance supply.** In Syria for example they comprised only 3 percent of outstanding microfinance loans in 2006. Providers of Islamic microfinance also tend to be small in size. Indonesia, Bangladesh, and Afghanistan – none of which are in MENA – account for 80 percent of the global outreach of

> 10 Sources: IFC, 2007, Assessment of MSE Financial Needs in Yemen; IFC, 2006, Access to Finance Study in Algeria; IFC, 2008, Lebanon: A Diagnostic Study on the Demand for Financial Services by Micro and Small Entrepreneurs.

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In 2019, we used Census and labor force survey data to select districts in the Upper River Region (URR) and Central River Region (CRR) that had high levels of irregular migration. These regions are largely rural, and have a driving distance of around 300km or more from the capital city of Banjul, and 450km from Dakar. We then randomly chose 391 settlements in these districts and did a door-to-door listing exercise to identify households with males aged 18 to 33, the prime age for migration. A face-to-face baseline survey in April and May 2019 collected demographic and labor force information, previous migration experience, and migration intentions of 3,641 youth. In September and October 2020, an in-person follow-up survey was conducted. We were able to re-interview 2,757 of the youth (76% of the original sample), which form the sample used for this paper.<sup>9</sup> The first column of Table 1 provides baseline summary statistics for the sample interviewed at follow-up. This sample consists of males with an average age of 23, who are almost all working in agriculture, with only 28 percent having worked for pay in the past month. Schooling levels are low, with an average of just over 3 grades of schooling, and 31 percent are married. Only 3 percent made a previous unsuccessful attempt to migrate to Europe, but 22 percent have relatives in Europe, and on average they know 3 people who have died going the backway and 10 people who have made it. 29 percent were receiving remittances at baseline. The initial desire to migrate to Europe is high. We asked at baseline “Ideally, if you had the opportunity, would you be willing to migrate and live elsewhere outside The Gambia?” If respondents said yes, they were asked which country they would ideally like to move to. 55 percent said they would like to move to somewhere in Europe, and 7 percent would ideally like to move to Senegal. This “ideal” destination may be different from where they intend to migrate given the costs and difficulty of moving, and we will examine migration intentions and their measurement in the next section.

# **3.2 The COVID-19 Pandemic and the Gambia**

The Gambia registered its first case of COVID-19 on March 17, 2020 when a female traveling back from the UK tested positive for the virus and the first fatality was registered on March 23,

> 9 We collected some information about the youth’s location and occupation from other household members for almost all of the youth that we could not interview in person, but these proxy reports do not include migration intentions, and so are not used in this paper.

9
**Table 8 OLS: Are student characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grade 4</u>**

||**UP**||**MP**||**K**|**A**|
|---|---|---|---|---|---|---|
||**Hindi**|**Math**|**Hindi**|**Math**|**Kannada**|**Math**|
|Age|0.02|0.02|-0.02|-0.01|0.00|0.00|
||(0.01)**|(0.00)**|(0.00)**|(0.13)|(0.94)|(0.83)|
|Generalcaste|0.08|0.05|0.04|0.04|-|-|
||(0.00)**|(0.00)**|0.12|(0.05)*|-|-|
|OBC|0.05|0.04|-0.02|-0.02|-0.02|-0.02|
||(0.10)|(0.05)*|(0.39)|(0.36)|(0.13)|(0.22)|
|ST(scheduled tribe)|-|-|-0.07|-0.04|-0.04|-0.02|
||||(0.02)*|(0.16)|(0.10)|(0.30)|
|Wealth (1 if land above<br>median)|0.04|0.03|0.02|0.01|0.02|0.03|
||(0.01)**|(0.01)**|(0.34)|(0.68)|(0.18)|(0.07)|
|Gender(1 if male)|0.06|0.10|0.05|0.05|-0.01|0.00|
||(0.00)**|(0.00)**|(0.01)**|(0.00)**|(0.32)|(0.74)|
|Mother literate|0.10|0.08|0.12|0.09|0.02|0.01|
||(0.00)**|(0.00)**|(0.00)**|(0.00)**|(0.17)|(0.31)|
|Father literate|0.08|0.07|0.02|0.01|0.00|0.02|
||(0.00)**|(0.00)**|(0.18)|(0.24)|(0.86)|(0.08)|
|School/village fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2579|2579|2321|2321|2176|2176|
|R-squared|0.28|0.27|0.34|0.43|.35|.44|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%; 23
In particular, taking into consideration the fact that a large part of the empirical literature on interfuel substitution pertains to developed economies, our benchmark group consists of six high-income countries: Canada, France, Japan, Italy, the United Kingdom, and the United States; Germany is not included in this group, because of distortions in the quantity data following the unification of the country in the 1990s. The second group includes five upper-middle to high-income economies: Poland, Hungary, Mexico, Turkey, and Venezuela. The third group of countries includes four lower-middle to low-income economies: China, India, South Africa, and Thailand.

Individual fuel (total final) consumption data come from the World Energy Statistics and Balances, published by the International Energy Agency (IEA). All fuel quantities are expressed in kilotonnes of oil equivalent (ktoe). Individual fuel prices, in U.S. dollars per tonne of oil equivalent (toe), come from Energy Prices and Taxes, also published by the IEA. For consistency across the different countries, we use industrial sector prices as the representative fuel prices. Moreover, we use the high-sulphur fuel oil price as the representative price of oil and the steam coal price as the representative coal price. For those countries for which industrial sector prices are not available, we use different proxies; for example, we use the electricity-generation sector prices as a proxy for the coal price for South Africa and the industrial price of automotive diesel as a proxy for the oil price in Thailand.

Whenever possible, we use a four-input model, consisting of petroleum products (‘oil’), natural gas, coal and electricity. For some countries, however, we use a three-input model, either because of the structure of the economy (i.e., Turkey did not use natural gas until the 1990s) or because of data availability issues. In particular, for Canada and Venezuela, we do not have data for the price of coal and therefore use a three-fuel model consisting of oil, natural gas and electricity. For China, India, Italy, South Africa, Thailand and Turkey we do not have data for natural gas prices and thus use a three-fuel model, consisting of oil, coal and electricity.

Finally, for the high-income countries (Canada, France, Japan, Italy, the United Kingdom and the United States) and Mexico, Hungary, Thailand and Turkey, the sample period is from 1980 to 2006, a total of 27 observations ( = 27). For India the sample period is from 1981 to 2005 ( = 25), for Venezuela from 1981 to 1999 ( = 19), for South Africa from 1980 to 2005 ( = 26), for Poland from 1986 to 2006 ( = 21) and for China from 1990 to 2006 ( = 17).

# 5 Interfuel Substitution at the National Level

Tables 1-15 contain a summary of results in terms of parameter estimates and positivity, monotonicity and curvature violations when the NQ model is estimated without the curvature conditions imposed (in the first column) and with the curvature conditions imposed (in the 9
# 4.2 Randomization Balance Tests

As shown via balance tests in Appendix Table A2, the subsample of households who were subjected to HAP monitoring (n=204) are representative of the overall sample (n=504<sup>8</sup> ). We test balance across treatment and control households using 39 variables for the subsample that had HAP monitoring. A total of 23 variables are from the baseline survey and 16 are from the endline survey. Though randomization occurred prior to the baseline, we add endline variables related to temperature, humidity, cooking/baking area floor area, other cooking area characteristics and cooking characteristics from 2016 that were not collected in 2013, because these variables may affect 2016 HAP measurements and are invariant to whether a household is in the treatment group.

We find that all 2016 variables are balanced across treatment and control subsamples, suggesting that HAP monitoring will yield an unbiased estimate of the effect of Mirt on HAP. From the 2013 baseline survey, only one variable – existence of metal roofs on main houses - is not balanced at the 5% significance level, which is a rate that is just as likely to be due to chance given the 5% significance threshold; though a majority of both treated and control households have metal rather than thatched roofs, treated houses are more likely to have metal roofs. All balance test results are presented in the Appendix.

# 4.3 Treatment Effects of the Mirt ICS on Household Air Pollution

Table 1 presents the basic findings from the HAP monitoring. As shown in Table 1, consistent with the literature (Ojo et al., 2015; Chen et al., 2016; MacKracken and Smith, 1998), the mean and maximum values are extremely high. The average of the mean concentrations across the sample is 1,231  g/m<sup>3</sup> . This figure is about 50 times the WHO (2015; 2006) 24-hour guideline of 25  g/m<sup>3</sup> .<sup>9</sup> The average of the mean concentrations for households with Mirt stoves is slightly lower than for those who did not receive a Mirt stove and use only the traditional technology (1,162  g/m<sup>3</sup> versus 1,297  g/m<sup>3</sup> ). The average of measured median PM2.5 concentrations is 149.39  g/m<sup>3</sup> , with Mirt stove households again a bit lower at 145.12  g/m<sup>3</sup> .

> 8 In the endline, when HAP monitoring was conducted, enumerators could not contact 23 households. Attrition was balanced across treatment and control arms of the study, and uncorrelated with household characteristics likely to determine HAP.  In balance tests, samples sizes are therefore less than 504.

> 9 We omit three observations that could be outliers, because the mean is over 19,000  g/m3 during the three-day monitoring period.

10
transfers from the Soviet Union and helped to restore aggregate demand, mainly through an expansion of consumption.

# **3. Growth and Development Challenges and Constraints**

The key long-term challenges for development policy in Tajikistan are to sustain rapid economic growth, to ensure that the benefits of this growth are equitably distributed, which requires growth of labor intensive production to create more employment, and to reverse the deterioration in social indicators, notably in educational attainment and health status that has taken place since the country became independent. The type of growth that has occurred during the 2000s, characterized by a shift back towards the economy’s production possibility frontier, is unlikely to continue for much longer. Sustainable long-term growth will require increased investment and improvements in labor productivity to boost the supply side of the economy.

In common with other CIS countries, Tajikistan inherited a large capital stock relative to the size of its economy from the Soviet era; hence the economy is capital intensive for a low-income country. However much of the capital stock is in the public sector and is not well suited for a market economy. Tajikistan needs more investment, but the priority should be private investment in marketable goods and services, such as non traditional exports and services for domestic consumption. Private investment has been very low; amounting to only 5-6% of GDP during 2003-2006.<sup>4</sup> While the macroeconomic environment for private investment has improved markedly, some of the institutional features of the investment environment are a major constraint to private investment, as shown by business surveys such as the 2005 Business Environment and Enterprise Survey (BEEPs). The business climate is characterized by unpredictability, a lack of transparency and an uneven playing field for businesses. Instead of a clear set of rules for all businesses, individual firms make their own special arrangements with government officials (World Bank, 2005D: 22-

> 4 This estimate is from the IMF, and probably includes an element of investment by public enterprises.

6
corporate income.15 Second, we always include payroll taxes, which requires at times to add new data sources (e.g., we digitized payroll tax revenues from the UN System of National Accounts and from Fisunoglu et al. (2011)). Third, we always include taxes on property, often the main source of local government tax revenues.

## **3.2.3 Data coverage**

Figure A1 shows that our dataset covers 86% of world GDP in 1965 and 98% in 2015, as the number of countries grows from 78 to 156.16 The main change in the sample of countries covered corresponds to the entry of ex-communist countries in the early 1990s. We also include China from 1994 on, the date of the creation of the modern Chinese tax system (World Bank, 2008).17 Late decolonization and end of civil wars are other reasons to enter the panel later than 1965.

The dataset is thus composed of two (quasi) balanced panels: the first covers the years 1965-1993 and excludes communist regimes. It accounts for 85-90% of world GDP during those years. The second covers 1994-2018 and includes former communist countries and China; it accounts for 98% of world GDP. At their time of entry into the dataset, ex-communist countries account for 8% of world GDP (4.5% for China and 3.3% for Russia).

# **4 Global trends in tax revenues, factor shares and**

With this new dataset, we document the global evolution of tax revenues, factor shares, and effective taxation on capital and labor from 1965 to 2018. Our objective is to show time series for each outcome which can be interpreted as the global value worldwide, in each year. For example, the global effective tax rate on capital equals worldwide capital tax

> 15In some cases, this split is not available in the headline, official income tax revenue aggregates, so we look to expert studies elsewhere.

> 16In the most recent years we do not cover 100% of world GDP, as we did not try to collect data from countries with fewer than 1 million inhabitants when these were not available through online sources. 17See Appendix B.3 for a case study of China.

14