rafmacalaba/data-use-mentions-extended huggingface dataset:
These measures of macroeconomic effective taxation capture the overall tax burden on labor and capital, building on the work of Mendoza, Razin, and Tesar (1994) and Carey and Rabesona (2004).7 These ETRs have a number of advantages. First, they capture the economically relevant tax wedges on each factor of production (i.e., the wedges that matter for production decisions), such as the difference between the costs to employ a worker and what the worker receives.8 Second, because national account statistics are compiled following harmonized concepts and methods, they are conceptually comparable over time and across countries. Third, since they rely on the amount of tax effectively collected by governments, they incorporate the net effects of all tax rules— such as base reductions, exemptions, and tax credits—and of tax avoidance and evasion.9

# **3.2 Data sources**

## **3.2.1 National income components**

To estimate factor shares for 156 countries since the 1960s, we create a harmonized panel of national accounts which combines data from the UN System of National Accounts, the World Inequality Database, and other sources.

From the World Inequality Database (WID, 2020), we retrieve United Nations (UN) System of National Accounts (SNA) data that covers 4,000 country-years. These data come from the production and income accounts of the online UN SNA (2008), “Main Aggregates and Detailed Tables.” In addition, the UN Statistics Division provided us access to their archival data on the components of GDP, with over 2,000 country-year observations from the 1960s and 1970s, presented following the 1968 System of National Accounts (UN

> 7Compared to pre-existing work, our ETRs are global, cover over a half-century, systematically integrate mixed income, and account for heterogeneity in the capital component of PIT revenue using new data on the location of the PIT exemption threshold and on the relative taxation of dividends vs. labor income.

> 8The
Our interest in this paper is in measuring the effect of the 2008-2009 recession on the wellbeing of different groups in Thai society. Data on consumption are available for every year, unlike information on income, which is only available for 2007 and 2009. We thus use a measure of wellbeing that focuses on real consumption. Following Friedman and Levinsohn (2002, p.403), who used such an approach in their study of the effects of the 2007-2008 Asian financial crisis <!-- Start of picture text --> 4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>2005 2006 2007 2008 2009 2010<br>% of labor force<br><!-- End of picture text -->

**Figure 3.  Unemployment rate in Thailand, 2005:M1 – 2010:M12** Source: Reported by Bank of Thailand.  Shaded area marks period of recession.

on poverty in Indonesia, we measure the change in wellbeing for any given household (indexed by _h_ ) using This says that the proportionate change in real consumption is approximately equal to a weighted average of the proportionate changes in the prices of the _n_ goods, where the weights ( _ai_ ) measure the importance of good _i_ in the household’s budget.

The Bank of Thailand publishes a series that measures private consumption per capita (in 1988 prices), and this is shown in the top panel of Figure 4, along with a simple trend line. The computation involves deflating nominal consumption expenditures by the consumer price index. By this measure, real consumption per capita was not only below trend in the first three quarters of 2009, but actually fell.

Losers and Winners in Recession: Thailand 2008-09 8
at what kinds of skills are available to Tanzanian firms, using educational attainment as a proxy for skills. Figure 1 shows the growth in average years of education of the population aged 25 and over in Tanzania along with a sample of other countries in Sub-Saharan Africa (SSA) and Vietnam. Tanzania’s average is about 5 years of education, behind Kenya with 6 years of education and other middle-income countries of SSA with 8 to 10 years of education. It is estimated that it would take Tanzania 45 years to attain an average of 8 years of education at the pace witnessed over the most recent 20-year period.

**Figure 1:  Average Years of Education in Tanzania and Selected Countries**

> 10 See the ILO’s (2012, page 11) definition of skills in the International Standard Classification of Occupations, 2008 (ISCO-08).

> 11 This follows the World Bank’s Skills Toward Employment and Productivity (STEP) framework described by Banerji et al. (2010).

> 12  STEP household and employer surveys have been rolled out in several countries since 2012 including (among others) Sri Lanka, Vietnam, Armenia, Colombia, Lao PDR, Ghana and Yunnan Province in China.

> 13 For example, a job requiring engineering skills and competencies cannot be filled by a person with basic education even after 20 years of experience or training.

6
and reported income in the CSES data. The tax on immovable property and the vehicle tax are modeled based on the estimated value of the property which is reported in the data, and the type(s) of car(s) which are reported by the household. There is not enough information in the data to assign the exact value of the vehicle tax, so the tax is imputed based on the characteristics that are available in the CSES.

- B) Property taxes in Cambodia are levied on means of transportation and on fixed assets. These are assigned to households based on a joint declared sum of property taxes reported in the CSES data, which is disaggregated into means of transportation tax and property tax using information on vehicle ownership and vehicle characteristics. The residual amount is assigned as property tax.

- C) The CSES data does not provide much detail concerning welfare transfers and itemizes only receipts of scholarships. Therefore, direct transfers are only divided into scholarships and (other) welfare transfers. We adjust transfer receipt values among households eligible to the Conditional Cash Transfer (CCT) to reflect the applicable values of the CCT. Pensions constitute part of overall welfare payments in the CSES 2019/20 data and cannot be separately identified. In Cambodia pensions however are a very small proportion of household incomes – in 2017 for example (when they could be separately identified) they amounted to 3% of total household incomes.

- D) Indirect taxes and subsidies are calculated based on the general rules applied to consumption taxation and the information provided in the CSES on household expenditures; this applies both to the VAT and excise duties, as well as to the imputation of electricity subsidies calculated based on location and reported cost and usage of electricity. The purchase of food products in rural areas is treated as informal and thus not subject to VAT in the model.

- E) In-kind transfers are allocated to households based on either age eligibility (education) or declared usage of these services (health). In the case of education
that grant secure property rights, permit formal contracting mechanisms, or access to formal financing mechanisms - can be considered a factor of production that becomes increasingly important as firms grow and that has a cost measured in terms of compliance with legal norms. For many high 0, and therefore permanently small firrns, issues of formality may therefore be largely irrelevant.

# **III. Data**

The data are from the 1992 National Micro-Enterprise Survey that was collected by the Mexican National Statistical Institute in 1992:1. This survey was constructed by selecting 11,000 owners of micro-enterprises, defined as firms of under six individuals (16 for manufacturing firms), from the last quarter of 1991 of the National Urban Employment Survey and examines reasons for becoming self-employed, incomes, capital structures, costs, enterprise problems, credit needs, migrant status, and employment patterns, among others. To focus on the firms commonly thought of as informal, we restrict the sample to males ages 16 - 65 with no more than 12 years of formal education.

The analysis include four broad classes of variables, used both raw and transformed into indices (see appendix for more detail):

# _A._ **_Characteristics_** _of_ **_the Entrepreneur:_**

_1. Years of Education of the Entrepreneur_ (Education).

_2. Years of Work Experience of the Entrepreneur_ (Experience).

_3. Why initiated enterprise_ (Voluntary). Captures whether the entrepreneur is voluntarily employed in the sector.

though if the cost of capital were fully imputed, the firm might not be viable. The worker is borrowing against future (past) income to subsidize an unviable firm.

_5_
or production managers. In this process, the most important consideration is to match operators to the work stations that they are skilled at (e.g. overlock machines are only operated by workers skilled in using this machine).

The management of the production floor typically prefers to keep the composition of wellfunctioning lines unchanged. However, there are some situations when it can be necessary to move operators for production reasons, for example to temporarily cover absences of operators on other lines, or when the style produced changes and with it the production requirements. Operators might also be moved to lines with a lower target if they struggle to meet targets or increase alteration rates. Line operators and supervisors are sometimes able to express their preferences for the assignment of operators to lines, but — barring any serious allegations of misconduct — the production requirements always take precedence in dealing with any potential requests. According to the industrial engineer, it would be especially unlikely to observe sorting of line operators towards lines of new supervisors in their trial period. During this trial period, supervisors are expected to prove themselves with the team they have been given, and any requests by themselves or operators to move to their lines would be unlikely to be granted.

I provide three pieces of evidence that support this strategy.<sup>41</sup> First, I show that the exposed and non-exposed operators are balanced on a number of observable characteristics in Table 2. Since there is no baseline data for the random operators, these characteristics are from the household surveys and more limited than the data available for the promotion analysis. Out of the 15 meanequality tests in Table 2, one test — for assets brought into the marriage — rejects at the 10% significance level using the conventional p-values in column (5), and one additional test — for age — rejects when I use the randomization inference p-values in column (6). This is in line with what one would expect to happen by chance. However, the normalized differences in column (
# **4. Data and Econometric Methodology**

To test the predictions from the model we use survey data collected by us from the same caste _of_ potters, from three villages in the South Indian State of Karnataka, viho were the focus of the qualitative interviews. All three villages exchange brides indicating that they are all part of the same marriage market. The survey consisted of a three part questionnaire, a family questionnaire directed to the head of the household, a woman's questionnaire for all women above the age of fifteen, and a husband's questionnaire. All 149 potter families in the three villages were interviewed, which included 177 adult women, and 130 married men. After dropping the never-married and widowed women we are left with a sample of 142 women of which 137 answered the violence question'<sup>4</sup> . In addition to questions relating to socio-economic characteristics and marriage transactions, all the married women were asked if they had ever been severely physically assaulted by the husbands at any time during the marriage, along with questions about the extent and severity of the violence. The question on the incidence of domestic violence elicited a much more accurate response than the questions about its extent and severity". Thus in this paper we will focus on the determinants of a binary variable indicating whether the wife has ever been severely physically assaulted by her husband. Men were not asked about wife-abuse in the survey, but were interviewed about it in the qualitative work. As far as possible, all the women were interviewed by female interviewers and the men by male interviewers.

It is clear that while our data are detailed and intimate, these advantages are traded off with the size of the sample'<sup>6</sup> and its representativeness. However, a more conventional sampling frame would not have permitted us to collect sensitive information like the incidence of wifeabuse, as we would not have established the level of trust necessary to gather such information with reasonable accuracy.

**
89.9%|65.5%|
||Maharashtra|36.2%|25.7%|57.0%|61.1%|
||Orissa|68.5%|54.6%|86.6%|24.1%|
||Punjab|64.8%|86.0%|96.6%|68.1%|
||Rajasthan|58.6%|82.2%|94.8%|31.4%|
||Tamil Nadu|73.6%|27.2%|79.5%|73.8%|
||Uttar Pradesh|39.3%|71.9%|84.9%|49.2%|
||Uttaranchal|54.5%|73.3%|90.4%|66.0%|
|India|West Bengal|46.3%|66.1%|88.3%|51.4%|
||West|16.3%|19.8%|32.7%|72.5%|
||Central|13.2%|8.8%|21.1%|26.8%|
|Nepal|East|1.4%|3.2%|4.4%|44.2%|

Source: Author’s elaboration from Enterprise Survey (2014) In all the SAR, large cities with more than 1 million population display a larger proportion of firms introducing new products than smaller cities (Table 18). A similar pattern is observed for process and organizational innovation except in India and Nepal. The innovation map in terms of product and organizational innovation is different from that observed in ECA, where innovation rates are higher in small cities. This contrasts the innovation picture portrayed in Africa, which presents similarities to the South Asia region.

23
Policy Research Working Paper 7563

# **Abstract**

This research estimates the impact of international child sponsorship on adult income and wealth of formerly sponsored children using data on 10,144 individuals in six countries. To identify causal effects, an age-eligibility rule followed from 1980 to 1992 is utilized that limited sponsorship to children twelve years old or younger when the program was introduced in a village, allowing comparisons of sponsored children with older siblings who were slightly too old to be sponsored. Estimations indicate that international child sponsorship increased monthly income by $13–17 over an untreated baseline of $75, principally from inducing higher future labor market participation. Results show evidence for positive impacts on dwelling quality in adulthood and modest evidence of impacts on ownership of consumer durables in adulthood, limited to increased ownership of mobile phones. Finally, results point to modest effects of child sponsorship on childbearing in adulthood.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at pglewwe@umn.edu.

_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 by the Research Support Team
RMNCH-N investment decision-making, through a three-pronged approach that involved:

1. Identifying the top ten determinants that need to be addressed on a priority basis to improve RMNCH-N utilization, quality of care and outcomes nationally and for divisions (or states)

2. Comparing the relative importance of demand- and-supply-side determinants on key RMNCH-N indicators in the last decade (across two time points) in Bangladesh 3. Proposing key strategic steps for improving the RMNCH-N utilization and quality in the country.

This study used machine learning analysis as the selected data sources (given below) were high-dimensional and large.

# **2.Methodology**

## **2.1 Data sources**

Two sources of secondary data were used in this study: the Bangladesh Health facility Survey (BHFS) and the Demographic and Health Survey (DHS). Two rounds (2014 and 2017) of BHFS and DHS survey data were used. Data sources and timelines were selected considering the need for a comparative analysis across two time points using multiple rounds of household and facility surveys. Both DHS and BHFS cover similar timeframes, enabling such a comparison. Averages of BFHS indicators by district and location (urbanrural) were merged with the DHS data.

3
in absolute and percentage terms than the increase from 2001 to 2006. Such an increase is consistent with broad state level trends from the Cost of Cultivation survey that show a decline in the amount of labor used in agriculture and an increase in the amount spent on machine inputs to production over the period that MNREGA was implemented.

The notion that farmers responded to the impact of MNREGA on agricultural labor markets by mechanizing is supported by their own statements as well. Media interviews with farmers from areas that have seen the largest increase in burning frequently include quotes like the following (Jitendra et al., 2017): Hari Ram Karore, a 71-year-old farmer who owns more than 10 hectares (ha) in the same village, says, “We started using combine harvester machines to tide over the labour scarcity. The machine finishes the task of reaping, threshing and winnowing in a few hours and is also economical,” he adds.

and Residents of villages around Kota say that mechanisation has killed the practice of using wheat stalk and straw as fodder, and burning is the only way out. “The cuttings left by the machines are too sharp. Not only do they injure us, even animals find it difficult to graze on,” says Shital Devi.

These quotes are supported by more formal work interviewing farmers (FLA, 2012) that indicates farmers believe the supply of unskilled agricultural labor has declined as a result of MNREGA. This is consistent with survey data from the 2011 India Human Development Survey that suggest a large share ( _≈_ 33%) of surveyed villages believe there to be fewer agricultural laborers than in the past and individual households report a 30% decline in the hours worked in agriculture. Fewer households in the IHDS report traveling for work after MNREGA as well.

> 35There are alternatives to clearing the residue after a combine harvest. Namely tilling the standing residue back into the field. However this requires specialized planting equipment that is both expensive and not widely present in India (Jain et al.,
broadly hold at country-level.

# **3.5 Summary of theoretical prediction**

We developed a simple theoretical framework, built around a student-level education production function, to explore why management practices might matter in schools. Using this framework, we showed that people management practices may be contributing to higher student test scores through two channels: teacher selection and teacher incentives. The predictions that these channels imply for intermediate school outcomes—fewer teacher shortages, higher teacher motivation, and higher teacher effort in schools with strong people management than in schools with weak people management—are all well-supported in our PISA data for Latin America.

We also showed that operations management practices may be contributing to higher student test scores via two channels: teacher selection and household incentives. The empirical support for the predictions that these channels imply for intermediate school outcomes—fewer teacher shortages, higher teacher motivation, higher teacher effort, and higher household effort in schools with strong operations management than in schools with weak operations management—is also strong.

While this does not represent definitive causal evidence, this combination of theory and descriptive empirical analysis offers an insight into why management appears to matter in schools and so we cautiously move on to policy. For example, what type of school management practices might be changed to drive improved student learning? Our analysis suggests that people management is a good place to start. While it may not be feasible (on political or budgetary grounds) for governments to introduce performance pay in public schools, it may be possible to conduct assessments to judge teacher effectiveness, and for these appraisals to lead to changes in public recognition, and to opportunities for professional development, likelihood of career advancement, and greater responsibilities and leadership. Such practices also reward and develop good performers and create a good employee proposition and could improve both teacher selection and incentives.

Beyond the difference in people management across private and public sectors, a striking feature of the PISA data is that there is substantial variation in the strength of operations management practices within the public sector
# **<mark>Table 4: Correlation of Practice Changes Within Groups</mark>**

|Dependent Variable: Change in Practice between Baseline and En|dline|||
|---|---|---|---|
||(1)|(2)|(3)|
|Mean Change in Practice for other Group Members|0.100*<br>(0.050)||0.104**<br>(0.049)|
|Maximum Baseline Level of Practice for Other Group Members||0.001<br>(0.021)|0.014<br>(0.019)|
|Sample Size (Firms*Practices)|5069|5210|5069|
|Mean Change in Practices|0.168|0.171|0.168|
|Notes:||||

<mark>Regression uses the stacked panel of 141 practices for firms in the group treatment. Robust standard errors in parentheses, clustered at the firm level. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.</mark> 38
behaviors, controlling for baseline covariates and block fixed effects. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 3. Non-agricultural low-skill wage labor, which includes contract work, carloading, car-washing, peim-peim riding, carrying loads, guarding, housecleaning, and construction 4. Agricultural work, which includes farming and fishing, 5. Illicit work, which includes selling drugs, stealing, gambling, gold rubber, and scavenging.

Table E.7 reports ITT estimates on the average of the two weeks of data. While we generally observe no changes in overall average hours worked per week (the one exception is those assigned to cash only work approximately 15% more hours per week in the short-term), treatment effects how participants allocate their time. In the short-term, all three treatments cause participants to shift from illicit work to non-agricultural low-skill business. Those assigned to both therapy and cash experience the largest decline in illicit work. Time spent in illicit work falls 38% 2–5 weeks after implementation relative to the control group, and is 17% less than the control group one year later (although the latter is not statistically significant). Although the cash-only group more than doubles its weekly hours spent in non-agricultural low-skill business in the short-term, these effects phase out 12–13 months later.

# **F Survey data validation details**

## **F.1 Variables**

We selected six variables for validation, all with recall periods of two weeks. We chose outcomes with varying degrees of salience (or memorability) and potential social stigma and experimenter bias. The variables were:

1. _Stealing._ The survey asked how many times in the last two weeks the respondent stole someone’s belongings or deceived or conned someone of money or goods.<sup>19</sup> Based on our fieldwork, we hypothesized that stealing would be the most salient and least socially desirable of