rafmacalaba/data-use-mentions-extended huggingface dataset:
argues that this may be partly explained by business regulations, such as zoning laws, and diculties in setting up national distribution systems because of the low quantity and quality of rail and road networks. The limited role of reallocation in Brazil's retail sector may explain its low productivity growth.

# 3.2 Informality and productivity

The previous analysis provided evidence of signicant misallocation of factors of production among Brazilian formal rms. However, in order to have a broader picture of misallocation in Brazil, we have to consider the large informal sector.<sup>11</sup> Meghir et al. (2015a) use data from the Monthly Employment Survey (Pesquisa Mensal de Emprego, PME), which provides a rotating panel of individuals sampled from the six main metropolitan regions of Brazil.<sup>12</sup> They show that the lowest part of the productivity distribution is populated only by informal rms. However, over a large segment of the support of the productivity distribution, formal and informal rms coexist. The increased probability of detection for larger informal rms

> 11See Ulyssea (2020b) for a recent review of the literature.

> 12PME has been discontinued in 2016. It was replaced by PNAD Contínua, which also has a rotating panel but is representative at the national level.

12
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
the reference group. Productivity differentials by occupation do not fall behind wage differentials, as revealed by the rejection of all tests oi equality between the two estimates (F(jobsupp)=2.67, Prob > F=0.1O and F(jobman)=O.01', Prob > F=0.93).

I use the occupational data to test for the possibility of a gap in returns between skilled and unskilled labor in Ghana. Skilled workers are those in the manager, administration, and professional, occupations. Unskilled workers are those in the commercial, production, and support occupations. The skilled employees may receive higher returns to experience, sector, training, education, unionization, occupation, and location than unskilled employees. The results are based on the inclusion of interaction variables in the analyses. A dummy variable that takes the value one if the employee is unskilled is interacted with the variables presented in appendix table B-1.

The findings indicate that the returns to education and, in particular, to having completed middle and secondary education, are lower for unskilled than skilled workers (see appendix table B-4). The comparison group is workers who have not completed any level of education. The unskilled workers who have completed middle school earn returns 23 percentage points lower than skilled workers do(see appendix table B-4). The productivity analysis reveals that unskilled workers are not statistically less productive than skilled workers. The economic return to completed secondary education is 24 percentage points lower for unskilled than for skilled workers.

# **4.3 Differentials by Trade Unions**

20
as other types of health insurance including voluntary insurance, insurance for workers within the Green Line,<sup>30</sup> group insurance, social affairs insurance, and prisoners’ insurance.

## **Education**

**The Ministry of Education and Higher Education (MoEHE) manages public schools, including regulatory overview of private schools.** This includes pre-school, primary (or basic) and secondary school (grades 1 through 12), technical and vocational, non-formal, and higher education. The MoEHE is the official body responsible for running, organizing, and developing the educational sector either through direct management or supervision. It also spearheads the national education strategic planning process. The Schools’ Census 2016-17 showed that there were about 2,963 schools in the Palestinian Territories, 73 percent of them administered by the government, and about 1,229,756 students were enrolled, about 65 percent of them in government schools.

# **2.6 International comparison**

**Tax revenue in the West Bank and Gaza as a share of GDP is high at about 20.7 percent.** This compares to an average of 18.1 percent for all countries included in the CEQ Institute’s data center. It is also high compared to the average of lower middle-income countries of about 17.9 percent of GDP (indicated by the dashed orange line in Figure 2-1).

> 29 While UNRWA’s mandate is to provide services to refuges, its health services are mainly focused on primary health care, and it purchases secondary/tertiary services for a limited number of cases.

> 30 Workers within the Green line refers to individuals working in Israel. The Green Line is the demarcation line set out in the 1949 Armistice Agreements between the armies of Israel and those of its neighbors (Egypt, Jordan, Lebanon and the Syrian Arab Republic) after the 1948 War.

18
voucher clients with pressure to improve service quality. The programs also negotiate reimbursements to SPs to maintain costs, which, together with the set minimum standards of care, should ensure cost-effectiveness in service delivery.

This paper presents the results of a quasi-experimental evaluation of the Uganda RHVP and findings from other studies including health worker job satisfaction and trends in out-of-pocket spending on maternal health services. The paper presents outcomes in four broad categories: knowledge, behavior (including utilization and access), quality, and out-of-pocket spending as commonly presented in the literature (Bellows, Bellows, and Warren 2011; Brody, Irige, and Bellows 2015). A fifth category, health status, is not addressed in this study as the sample size required to detect a noticeable difference in mortality or morbidity was beyond the evaluation scope. Future studies could consider undertaking such an endpoint or present results of modeling 6
[0.012]|[0.687]||
|Add’l Year of MTI||-0.119|0.064||
|with Script Change||(0.052)|(0.028)||
|�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�||[0.025]|[0.028]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_||-0.008|-0.001||
|||(0.006)|(0.002)||
|||[0.169]|[0.630]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_||0.001|-0.004||
|||(0.005)|(0.002)||
|||[0.767]|[0.028]||
|�<br>Years of Schoolingizy||||-0.273<br>(0.109)<br>[0.012]|
|First Stage F-Statistic||14.09||14.09|
|Number of Clusters|60|60|60|60|
|N|205,141|205,141|205,141|205,141|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007 and the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three
is higher is due to the exclusion of China, which has both a large population and low rate of extreme poverty. Nonetheless, the data in our analysis is representative of nearly 5.9 billion people or more than 3/4<sup>th</sup> of the world’s population. The coverage of people estimated to be living in extreme poverty, the population that is relevant for this analysis, is much larger – the data cover 98 percent of this population.

For each country, the surveys have been conducted in different years and the data are reported in local currency units in current prices. Following the methodology used to report on SDG 1.1, we convert all income and consumption data into 2011 constant local prices using Consumer Price Indices from each country, and then convert the resulting vector into an internationally comparable US dollars using 2011 purchasing power parity exchange rates (PPPs).<sup>9</sup> The CPIs are used to estimate real changes in income and consumption over time, while the PPPs account for relative price differences across countries. In addition to using the same CPI and PPP data as used by the World Bank for global poverty monitoring, we also use the same population and national accounts data. For more details on how the World Bank estimates poverty, see World Bank (2020a).

In the second part of the analysis, we use covariates of poverty status to investigate the reliability of the square-root allocation rule of household consumption in identifying the poor, relative to the per-capita allocation rule. For this analysis, we use detailed micro-level data from the latest surveys of a subsample of eight countries, namely Nigeria, Mali, India, Pakistan, Colombia, Tajikistan, Indonesia, and the Republic of Yemen.<sup>10</sup> We wanted this analysis to have

> 9 For more details on the CPI series, see: <u>https://worldbank.github.io/PIP-Methodology/convert.html#CPIs.
where ln is the natural logarithm; Δ represents the difference over time; N measures population, E employment, and WP working-age population; α is the output elasticity of capital and equals a standard value of 0.33; z is the structural variables listed in Table 2. Constant return to scale and a constant WP ratio are assumed.

The estimation period spans from 1996 to 2016, although it shortens when some structural indicators are added to the model specification, in particular the indicators from the World Economic Forum (WEF) database which are available only since 2006 – see Table 2.

As shown in equation 1, all components are separable and independent of each other. It is therefore possible to compute the effect on output growth of structural reforms, namely changes in structural variables, z. Each dependent variable is in fact regressed on all the competitiveness indicators (Table 2) to identify those structural variables whose explanatory power remains robust and statistically significant across different model specifications.<sup>6</sup> Table 3 lists dependent variables, their description, and source. Cross-country panel data regressions are used to estimate both the capital-output ratio and employment rate equations. The Hausman test is used for fixed and random effects.<sup>7</sup>

**Table 3. Dependent Variables**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Real output|Real GDP in billions of constant 2011 international dollars|WEO|
|Total stock of<br>capital|Capital stock, billions of constant 2011 US dollars, extended with<br>depreciation and investment series from the WEO|Penn World Table, WEO|
|Employment|Employees and self-employed in thousands, extended with employment<br>growth rate from the WEO|Penn World Table, WEO|

The methodology is subject to several caveats, the most important being the endogeneity of the regressors. First, the
**Figure 13. Currency structure of corporate loans**

<!-- Start of picture text --> FX loans Zloty loans<br>%<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text -->

**Figure 14. Currency structure of household loans**

<!-- Start of picture text --> FX loans Zloty loans<br>100 %<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text -->

**Figure 15. NBP reference rate and interbank interest rates**

<!-- Start of picture text --> NBP reference rate WIBOR 3M<br>% (p.a.) EURIBOR 3M LIBOR CHF 3M<br>12,0<br>10,0<br>8,0<br>6,0<br>4,0<br>2,0<br>0,0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: NBP, Bloomberg.

**Figure 16. Nominal exchange rate of zloty (monthly data)**

<!-- Start of picture text --> USD/zl EUR/zl CHF/zl<br>140<br>130<br>120<br>
48

**Table A.5. Recent and possible future distribution of Ho Chi Minh City’s Population in districts in this study area.**

||2010 Percent of Total<br>Ho Chi Minh City<br>Population|2050 Percent of Total Ho<br>Chi Minh City Population<br>(World Bank estimate)|
|---|---|---|
|Binh Thanh|6.4%|3.0%|
|District 1|2.5%|1.12%|
|District 10|3.1%|0.83%|
|District 3|2.6%|0.71%|
|Go Vap|7.4%|2.8%|
|Phu Nhua|2.4%|0.7%|
|Tan Binh|5.8%|3.23%|

# **Ho Chi Minh City Poverty Rate**

The most recent official estimate of Ho Chi Minh City’s poverty rate by district is from the Inter ‐ ministerial Poverty Mapping Task Force in 2003 (ADB 2010). This study estimates an overall poverty rate of 5.4%. The literature did not offer estimates of future poverty; therefore, we use estimates of recent poverty to provide a plausible range for this value. Estimates of recent poverty rates vary widely based on methodology and definitions of poverty. The United Nations Development Program conducted an Urban Poverty Survey that estimates the poverty rate of Ho Chi Minh City at 13.9%, using an annual income threshold of 12 million VND per person (2010, p.90).<sup>29</sup> One can also distinguish between the poor and non-poor according to their housing facilities, a differentiator that may be important when considering exposure to flood. The GIS data provided by the Steering Center for Flood Control suggests a poverty rate of 2.4% under the assumption that the
probability of being multi-dimensionally poor by 2.6 percent, and decreases the probability of being employed in the agriculture sector by 4 percent among male household members and 5.3 percent among females. Hence, reducing transportation costs plays an important role in income diversification and structural change.

Nevertheless, the effects of road interventions are neither systematic nor uniform. Drawing on the 2001 National Household Survey for Cameroon, Gachassin et al (2015) demonstrate that better road access increases the number of economic activities within reach of households living in the most isolated locations, based on a “pull” factor, which draws them into new and higherearning opportunities. However, the impacts of isolation on household well-being via labor market opportunities are heterogeneous and depend on local characteristics.

An important channel through which rural roads generate economic benefits is by improving the productivity of the agriculture sector. Dorosh et al (2012) examine the relationship between transport infrastructure, population location, and agricultural production in Sub-Saharan Africa. Both population and agricultural production are spatially concentrated near large cities. A 10 percent decrease in travel time from a local crop production location to a nearby city of more than 25,000 people increases local crop production by 23 percent in the long run. A 1 percent reduction in travel time to the nearest city with 100,000 people or more increases low-input (high-input) crop production by 2.9 (1.6) percent. In a similar vein, Kiprona and Maksumoto (2018) show that rural road infrastructure improvements in Southwestern Kenya increase agricultural productivity and market participation by rural smallholder farmers.

Developing countries, especially in Sub-Saharan Africa, have lagged in agricultural technology adoption, despite widespread experimentation with input subsidies. To understand the reasons for this, Aggarwal et al (2018) quantify market access in the 1,183 villages in rural Tanzania and examine the extent to which it constrains agricultural productivity. They find that less favorable input and output prices in remote areas resulted in lower profitability leading to lower agricultural technology adoption in these locations.
**<u>Table 1: Descriptive statistics from farm census and digitization</u>**

||<br>**Total **|<br>**5-20 ha**|**20-50 ha**|**50-100 ha**|**>100 ha**|
|---|---|---|---|---|---|
|**Type of respondent**||||||
|Farm owner|0.94|0.94|0.94|0.96|0.93|
|All operated by owner|0.87|0.88|0.85|0.82|0.74|
|Part operated by owner<br>|0.12|0.11|0.14|0.18|0.26|
|If part or none: operated by a renter|0.08|0.09|0.05|0.04|0.11|
|**Time of land acquisition**<sup>a</sup>||||||
|Before 1995|0.44|0.41|0.55|0.56|0.52|
|1996-2000|0.17|0.18|0.14|0.16|0.18|
|2001-2005|0.11|0.11|0.11|0.10|0.10|
|2006-2010|0.11|0.12|0.09|0.06|0.07|
|2011-2015|0.09|0.10|0.07|0.06|0.10|
|2016-2020|0.07|0.08|0.05|0.04|0.04|
|No. of years possessed|25.
1|19.6|19.3|-1.9%|-2.6%|-1.5%|
|Micro firms|33.1|32.4|31.4|30.0|-2.2%|-3.3%|-4.4%|
|Small firms|42.9|42.4|40.1|38.4|-1.2%|-5.4%|-4.2%|
|Medium firms|37.8|36.6|36.2|34.2|-3.1%|-1.1%|-5.3%|
|Large firms|45.7|44.0|44.3|41.1|-3.8%|0.7%|-7.1%|
|Government|45.9|45.2|45.3|42.9|-1.5%|0.2%|-5.1%|
|Other|22.6|23.0|22.4|22.4|1.8%|-3.0%|0.0%|
|Not specified|30.2|29.7|29.5|28.1|-1.7%|-0.7%|-4.7%|
|Self employed|33.2|33.1|32.4|31.1|-0.2%|-2.1%|-4.2%|
|Owner|64.1|60.2|55.0|53.9|-6.1%|-8.6%|-2.0%|

**Source: Own calculations using data from ENOE.**

32
survey included 15 street informals representing the relative low-end of the informal spectrum, and 15 kiosks representing the relative high end of the informal spectrum. See also note 66.

41.See, especially, IMF (1996), pages 31-32, which argues that the share of demand deposits fell as deposits were shifted into higher-paying time and foreign currency deposits.

42.Indeed, a more elaborate variant of the currency ratio method employs an explicit econometric model of currency demand. (See Schneider 1997 for a description.) The lack of long time series data for Mongolia has motivated the use of the simpler currency-ratio method in this study.

43.Using MI, as in the traditional currency-ratio method, results in much higher estimates. Using MI, Mongolia's underground economy was equal to about 56% of the "above ground" economy at the end of 1996.

44.Kauftnann and Kaliberda cite the Berkeley-Duke research project on the USSR Second Economy conducted during the late 1980s, as well as Janine Braithwaite "From Second Economy to Informal Sector: The Russian Labor Market in Transition," World Bank mimeo 1994, for providing the estimates of 10-15% for 1989. They then used the mid-point of 12% for their baseline for applying the "macro-electric" approach to estimating the size of Ukraine's "unofficial' economy.

45.Although this technique is usually motivated by the belief that participants in the "underground" economy prefer cash over bank deposits specifically so that they may hide income and avoid taxation, it is important to note that such motivation is not required. Informals may prefer cash simply because the level of activity does not warrant the transactions costs of dealing with banks. The simple currency-ratio technique can not discem the reasons that people use cash for transaction, it can only ascertain the degree to which such preferences, in the aggregate, have changed over time.

46.The surveys of kiosks and taxicabs, undertaken as part of this study, support the contention that the informal sector is large in Ulaanbaatar
<!-- Start of picture text --> Bulgaria 2020<br>Headcount poverty (AROP) by household w/child category<br>50 46.1<br>40 38.0<br>32.1<br>30 25.7 27.3<br>21.3<br>20<br>10<br>0<br>Both parents or 2+ breadwinner Lone parents<br>TM Market Income plus pensions TM Disposable Income mm Consumable Income<br>Source: Author estimations based on EU-SILC 2021 and HBS 2021 using CEQ methodology<br><!-- End of picture text --> <!-- Start of picture text --> Bulgaria 2020<br>Headcount poverty (AROP) by household w/child category<br>50 47<br>44<br>42<br>40 36 37<br>31<br>30 26 27<br>21<br>20<br>10<br>0<br>Both parents or 2+ breadwinner Lone male parent Lone female parent<br>TM Market Income plus pensions TM Disposable Income mm Consumable Income<br>Source: Author estimations based on EU-SILC 2021 and HBS 2021 using CEQ methodology<br><!-- End of picture text -->
>_(0.282)_|
|VF could lend out another 1m baht safely|-|||0.584***<br>_(0.152)_|
|VF wants to borrow more from BAAC, GSB, etc.|-|||0.356***<br>_(0.128)_|
|VF borrows to on-lend to members|0.146***<br>_(0.026)_|||-|
|Memo items:|||||
|Number of observations|888|||885|
|R<sup>2</sup>|0.095||||
|pfor endogeneity||||0.032|

Source: Village Fund Survey 2010 and SES 2009. Sample includes cases with loan recovery rate between 0% and 100%. Robust standard errors in parentheses. Variables included based on OLS backward stepwise with p=0.2 cutoff. * p<0.05, ** p<0.01, *** p<0.001.

30
Mysore.

# **2.3 Imputation of TEE to NSS data**

As REE and AL cannot be directly estimated for the same data set using the factorial method, and the coverage in terms of age, state, and time period of the NFHS and TUS is incomplete, the estimates of TEE over time use NSS data that is representative at the population level. This is done using the large number of common variables between the NFHS, TUS, and NSS Schedule 10 Employment Survey (hereafter NSS-E). Predictive equations for REE or AL are first estimated using the NFHS or TUS and the large set of individual and household variables in common withˆ the NSS-E,ˆ andˆthese predictive equations are then used to generate _TEEi_ = _REEi ∗ ALi_ for each individual in the NSS-E. The analysis here focuses on the “thick” NSSE survey rounds beginning with the oldest available (1983) through to the most recently available (2011-2012).

7
intensity of municipal reforms.

# **4.3 Matched census-reform data set**

We match the data on regulatory changes across the 1,800 municipalities in Peru in 2013 and 2014 with establishment census panel data from 2008-15, using the 6-digit location codes providing the exact location of establishments at the district-municipality level. Since we observe subnational reforms at three different levels of aggregation (departments, province municipalities, and district municipalities), we then aggerate the number of reforms (by the different types of regulatory changes) for each establishment. Thus, establishments can be impacted by 0, 1 or multiple (up to 20) different reforms by different subnational government tiers in our matched data.

13