rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 8. Change in the Gini Index, from pre-fiscal to final income**

|Burkina Faso (2014)|
|---|
|<br>Uganda (2016)|
|<br>Mali (2014)|
|Ethiopia (2016)|
|Niger (2014)|
|Gambia (2016)|
|Tajikistan (2015)|
|eSwatini (2017)|
|Lesotho (2017)<br>|
|Zambia (2015)<br>|
|Kenya (2015)<br>|
|Ukraine (2016)|
|Djibouti (2017)|
|El Salvador (2017)<br>|
|Bolivia (2015)|
|State of Palestine (2017)<br>|
|Morocco (2017)<br>|
|Vietnam (2018)|
|Moldova (2017)|
|Egypt (2015)|
|Laos (2019)<br>|
|Myanmar (2017)<br>|
|Cambodia (2019)<br>|
|South Africa (2015)|
|Argentina (2017)|
|Mexico (2014)|
|Namibia (2016)<br>|
|Bulgaria (2019)<br>|
|Costa Rica (2020)<br>|
|China (2018)<br>|
|Brazil (2018)|
|Colombia (2017)|
|Fiji (2019)<br>|
|Thailand (2019)<br>|
|<br>Iraq (2017)<br>|
|Turkey (2016)|
|Belarus (2015)|
|Malasia (2019)|
|Jordan (2017)<br>y|
|Russia (2014)<br>|
|Albania (2015)<br>|
|Guatemala (2014)<br>|
|Paraguay (2014)|
|Serbia (2016)|
|Indonesia (2017)|
|Spain (2017)<br>|
|United States (2016)<br>|
|<br>Panama (2016)<br>|
|Mauritius (2017)<br>Croatia (2018)|
|Romania (2016)|
|0<br>5<br>10<br>15<br>20<br>25|

_Notes_ : Original estimates based on data from CEQ Institute, CEQ Data Center on Fiscal Redistribution, https://commitmentoequity.org /datacenter; OECD data; World Bank data. Countries are grouped according to World Bank Income classification system starting with Low <u>Income Countries (LIC), Lower Middle Income Countries (LMIC), Upper Middle Income Countries (UMIC) and High Income Countries (HIC)</u> 20
expense on electricity and fuels. There is no question pertaining to the quantity of these energy sources consumed and neither is there any further disaggregation on the sources of fuels consumed by the establishment. In addition to this, starting from the year 2000, there is binary information available for each establishment as to whether non-availability of electricity connection and/or power-cuts was one of the problems faced by the establishment during the reference year. Finally, in the latest 2010 NSSO survey of informal establishments, there is a separate question on the fuel consumed by accommodation and food service activities. Appendix Table 2 provides the exact wording and details of questions pertaining to energy consumption in NSSO surveys, and Appendix Table 3 contrasts sizes of the sectors by industry.

The outcome of this data coverage can be summarized as follows. First, there is a consistent path to considering electricity usage across years for both unorganized and organized sectors. As such, we focus exclusively on this component of energy usage in this report, and we describe next the many steps undertaken to bring together the related questions and effectively utilize the contained information. In theory, one could also consider fuels in a similar manner over the 1989-2010 period. Although the collected information is somewhat weaker and there exists more variance in the underlying questions, analyses of these data would be a useful exercise for future research. Likewise, while not uniformly possible for the full sample period, a separation of prices and quantities for electricity could be conducted. Finally, and somewhat unfortunately, the most detailed energy data are collected for the period before 2000, and thus the most detailed studies would need to rely only on the first half of our sample period. We have chosen a data development route focused on electricity to maximize the duration studies and consistency across sectors.

# _Data Preparation and Observation Counts_

After reading the raw data from the surveys, we have 262,911 ASI observations and 955,234 NSSO observations. For the ASI, we retain only establishments indicating that they are open and operational, leaving 216,898 observations in the formal sector. Next, we drop observations for establishments outside of the manufacturing sector. Such industries may relate to mining and quarrying, fishing and aquaculture, or services sectors as depicted above. This yields 203,031 16
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27
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18
child marriage) as a struggle to survive (Thomas et al. (2004), Kruger (2007)). These predictions are consistent with findings by Duryea et al. (2007) showing that, in Brazil, an income shock that affected household heads increased not only children’s participation in labor markets but also children’s school dropout.

# **3 Empirical Strategy**

To identify the impact of COVID-19 on school participation, we draw from Mahmud and Riley (2021) in estimating the following individual fixed-effect linear probability model: where _SchoolAttiat_ measures the school attendance status of a schoolchild _i_ living in the Local Government area (LGA) _a_ , at time _t_ . By convention, _t_ equals zero in the preCOVID-19 period ( _i.e._ , academic year 2019-2020, before the protracted schools closure due to the pandemic) and one in the period following schools’ re-opening ( _i.e._ , when schools re-opened on October 12th, 2020<sup>19</sup> ). The coefficient of interest is _α_ 0, which captures the immediate effect of COVID-19’s federal lockdown measures in Nigeria _C_ 19 _Shockt_ on school attendance. _C_ 19 _Shockt_ is defined as a binary variable equals to 1 for the COVID-19 period (when schools re-opened) and 0 for the pre-COVID-19 period (before the implementation of lockdown measures, including schools’ closure).

However, there may be concerns that _α_ 0 may also be capturing the occurrence of any other time variant event (e.g., the occurrence of a _Boko Haram_ incursion in a given geographic area, climate shocks such as droughts or floods) or a household shock that occurred between time zero and time one and that may affect the school attendance decision.

To account for the effect of _Boko Haram_ in as a ro- potential confounding particular, bustness check, we estimate equation (1) by dropping from our sample all observations

> 19When schools re-opened, individuals may be enrolled either in academic year 2019-2020 or 2020-2021.

21
# **Table 7**

# **Questions 4–7: Responses**

This table reports the answers for questions 5 through 8. The value is measured as a proportion of the monthly payment of each question. A choice of _X >_ 1 is considered to be from people who did not understand the question, as it suggest they would prefer to pay _X_ for sure instead of something less than _X_ some of the time only. Thus, Panel A shows the results for all sample, and panel B shows the results for students who responded _X ≤_ 1 only.

||||**Panel **|**A: Fu**|**ll Sample**||||
|---|---|---|---|---|---|---|---|---|
|||Treatm|ent||Contr|ol|Di|ference|
|Question|N|Mean|Std. Error|N|Mean|Std. Error|Mean|Std. Error|
|Question 4|519|20.247|16.133|558|2.543***|0.592|17.704|15.570|
|Question 5|476|2.283***|0.377|486|1.752***|0.300|0.531|0.481|
|Question 6|419|2.068***|0.381|432|1.752***|0.300|0.605|0.437|
|Question 7|394|1.609***|0.312|404|1.975***|0.693|-0.366|0.766|

**Panel B: Only Respondents with** _X_ _<u>≤</u>_ 1

|||Treatm|ent||Contr|ol|Dife|rence|
|---|---|---|---|---|---|---|---|---|
|Question|N|Mean|Std. Error|N|Mean|Std. Error|Mean|Std. Error|
|Question 4|268|0.742***|0.018|285|0.776***|0.016|-0.034|0.024|
|Question 5|298|0.656***|0.020|323|0.665***|0.020|-0.008|0.028|
|Question 6|292|0.678***|0.017|300|0.729***|0.016|-0.0502**|0.023|
|Question 7|310|0.674***|0.016|328|0.735***|0.015|-0.060***|0.022|

- 10% Significance Level

- ** 5% Significance Level

*** 1% Significance Level

27
# **5 SMS Message Experiment Methodology and Results**

## **5.1 Methodology**

The design of the SMS message experiment was identical to the online experiment, except it focused on whether it was possible to replicate the main results of the online experiment with a different set of respondents and consequently it included fewer questions.<sup>3</sup> In addition, it was necessary to have fewer questions because there is often high rates of attrition in SMS surveys when many questions are asked.<sup>4</sup> This SMS survey experiment was conducted using U-report. U-report is an open-source, mobile messaging program developed and managed by UNICEF Country Offices (UNICEF, 2022). People registered with U-report regularly conduct surveys, receive community announcements and seek advice via SMS messages. It has been in operation in Zambia since 2012 and is primarily targeted towards young adults. Respondents are required to be literate so that they can both read and respond to SMS messages written in English. It does not cover a nationally representative sample of the population as individuals registered on U-report users are more likely to be younger, male and in urban areas (see Appendix A5 for details). To address this issue, survey data was weighted using inverse probability weights based on age, gender, and region calculated using the most recent Zambian census from 2010 (see Appendix A5 for details). As a robustness check, the main experimental findings are calculated without survey weights and the results (i.e., the sample average treatment effects) are qualitatively similar to those presented in the body of the paper.

An SMS message was sent on August 26, 2022 inviting the entire universe of 249,432 individuals registered with U-report in Zambia to participate in the survey. In total, 13,770 U-report users responded to the SMS message that explicitly asked respondents to share their thoughts about COVID-19 vaccines (see Appendix A5 for details). These individuals

> 3For example, respondents were not asked about whether they were involved in household decisions as this was not strongly associated with people’s willingness to get vaccinated in the in-person and online surveys and there were no significant heterogeneous treatment effects on this dimension in the online experiment.

> 4To minimize the rate of attrition, the survey only involved six questions and respondents were informed at the start of the survey it should take less than two minutes to complete.

30
10

# **111. INTERVIEW NOTES**

# A. _Safe ModA*ood_ **_Fiuwing_** _by Exernal_ **_Finuncial_** _Sources_

# AUSTRIA

The Safe Motherhood Initiative is acceptable within the development policy of Austria. Estimates of SM content should be possible and would include bilateral programs, contributions to UNFPA, and a proportion of voluntary contributions to UNICEF. In 1988 there were several bilateral programs -- five in Africa, one in Asia, and one in Latin America. In addition, Austria participatedi in a S10 million World Bank loani to Uganda.

ODA spending amounted to S198 million in 1986 and S196 million in 1987.

Technical staff does not record annual health sector statistics. The standard procedure is to record development programs by country. An annual approximation of spending on bilateral health programs is provided to DAC/OECD. Statistical information on program content is too sparse to permit conclusions on maternal health expenditures, except for UNFPA contributions.

||Estimated Financial Flo<br>S milli|ws to Dev<br>ons|eloping Co|untries|
|---|---|---|---|---|
||1985|1986|1987|1988|
|Total Health Sector<br>;bilateral, multilateral,<br>nongovernmental)|_4.5_|4.2|8.3|12.6|
|Safe Motherhood|.56|.47|2.8|5.3|

Dept. of Development Aid Federal Ministry of Foreign Affairs Minoritenplaz 9 1040 Vienna
saving is buoyed by demographics, precautionary saving, and relatively undeveloped financial markets.

- Government saving is high as well. About 5 percent of GDP of this saving is transferred to enterprises. High government saving has been the result of a growth-oriented economic policy emphasizing investment.

- But enterprise own saving is also high and has been rising further in recent years. Enterprise saving is boosted by an industry-oriented economic structure and a tradition of low dividend payments, especially for SOEs. Enterprise saving has risen substantially since the end-1990s as profitability has improved, and almost 3/4<sup>th</sup> of enterprise investment is financed by retained earnings.

- Capital transfers to SOEs of almost 5 percent of GDP also keep down enterprise financing by the financial sector. These capital transfers also mean that government capital spending (8 percent of GDP) and saving (almost 6 percent of GDP) are significantly higher than suggested by the headline fiscal data.

As elaborated in section IV.2.2, these patterns suggest that the government has several powerful instruments to adjust saving and investment patterns.

# **II. WHAT ARE THE PROSPECTS FOR SAVING AND INVESTMENT IN THE COMING DECADES?**

What is the main guide for assessing the future: theory, cross-country evidence, or recent patterns?

# **IV.1 Existing literature**

Several papers have recently looked at the prospects for China’s saving, investment, and resulting capital flows, with varying conclusions.

Fehr, Jokish, and Kotlikoff (2005) suggest China would remain a large net saver. Their results are based on the fact that China’s saving behavior, economic growth, and fiscal policy are currently very different from that in the other countries, with the current private sector’s saving behavior suggesting a very low rate of time preference.<sup>11</sup> They use a neoclassical model to analyze international capital flows in a “world” proxied by the US, the EU, Japan, and China. They assume that China’s high saving rate will go down only slowly. Initially, capital mainly flows from the 3 developed regions to China, because at the world interest rate a lot of investment in China generates more than the required rate of return. If China’s own saving does not fall too fast, and the world interest rate—which applies to all countries—does not decline too fast, China’s investment will fall faster than saving. Thus, within the next decades, capital will start to flow from China to the other regions, and in the coming decades China will export significant amounts of capital to the other countries. This is so even if Chinese saving behavior, captured by its

> 11 Indeed, Mcdonald and Lu (2005) find that, based on a model with mainstream neoclassical assumptions, the rate of time preference that would generate current rates of Chinese saving is negative, implying a higher weight on the economic welfare of future generations relative to the current generation.

11
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# **1. Introduction**

Economic growth is an essential part of economic development and poverty reduction. Dollar et al (2013) find that incomes of the bottom 2 quintiles of the income distribution increase on average at the same rate as the average for the overall income distribution. This indicates that growth accounts for a large part of poverty reduction in past decades and increases the need to understand better what drives growth.

Explaining the determinants of economic growth has been at the core of the economic science since its inception. Why do some countries grow at a faster pace than others? Why are there high-income countries and low-income countries? Many factors can contribute and have significant impact on growth. This paper contributes to this bigger discussion by trying to answer the question: are business regulations a determinant of economic growth?

We establish this link in a two-stage manner. First we establish the link between business regulations with new firm creation and then we establish the direct link to GDP per capita growth. The link to new firm creation serves as one of the mechanism through which business regulations can affect growth.

The link between business regulations and firm creation is not new in the literature. For instance, Klapper and Love (2011) link business start-up regulations to firm creation using panel data across countries. Branstetter et al (2013) provide evidence that reducing the time and cost of firm registration increased the number of business start-ups and jobs created, using data on a quasinatural experiment from Portugal. Bruhn (2011) analyzes a quasi-natural experiment in Mexico and shows that simplified registration processes increased both firm creation and job creation. Kaplan, Piedra and Seira (2011) study the same quasi-natural experiment to find that simplified entry regulations led to the shifting of informal firms to the formal economy. We complement these studies by analyzing business regulations beyond entry regulations and by testing the results over a large set of countries. We find that an improvement of 10 points in the overall measure of business regulations is linked to an increase of around 0.5 new businesses per 1,000 adults.

There are fewer studies linking business regulations to growth. Djankov, McLiesh, and Ramalho (2006), using cross country data find that simpler business regulations are associated with higher long term growth. Eifert (2007) links business regulations to investment and growth using panel data. The author uses indicator level data and finds that in general better performance in business regulations indicators is linked to higher investment and growth. We add to the existing literature on growth and business regulations by analyzing a long series of panel data and testing different aggregations of the business regulation indicators. Our results show that although small changes 2