rafmacalaba/datause-encoder-data huggingface dataset:
**Table E.3**

# Projected Changes in Labor-Intensive Trade, Internal Comparison

||1988|Net Exports|of Labor-Inte|nsive Products|per Worker|Net Exports|of Labor-Int|ensive Produ|cts/GNP|
|---|---|---|---|---|---|---|---|---|---|
||GNP|1988<br>|'Scenarios|||1988|Scenarios|||
|Country|per Capita|Actual|(1)|(2)|(3)|Actual|(1)|(2)|(3)|
|COSTA RICA|1616|132.0|150.9|173.1|244.2|3.05%|3.49%|4.00%|5.65%|
|EL SALVADOR|1059|-1.4|69.4|191.8|373.3|-0.04%|2.04%|5.64%|10.99%|
|GUATEMALA|881|-7.1|37.7|65.9|138.9|-0.26%|1.39%|2.43%|5.13%|
|HONDURAS|859|-3.0|21.1|70.3|200.3|-0.11%|0.75%|2.51%|7.14%|
|NICARAGUA|813|-6.9|-107.1|4.3|90.5|-0.26%|-4.05%|0.16%|3.42%|
|PANAMA|1927|-587.6|-557.6|-581.6|-498.0|-10.89%|-10.34%|-10.78%|-9.23%|
|MEXICO|2023|20.3|88.4|47.9|95.4|0.35%|1.53%|0.83%|1.65%|

**Scenarios: (1) Savings** Rate Only

(2) Historical Investment Rate (3) High Investment Rate Note: Net exports of labor-intensive goods per worker are in 1988 dollars.
role, this indicates a more equal footprint across travel classes. Systematic differences in footprints by travel class are today being recognized but treated imprecisely. For example, the standard “ICAO calculator” uses a 2.0 multiplier for passengers in any service class above coach class.<sup>18</sup> Table 4.1 gives numbers for the average space in aircraft taken up by seats in alternative travel classes, separating between single-aisle and wide-body aircraft. We here find significant and systematic differences in seat size by class and aircraft size. The difference in floor space occupied by premium versus coach seats is much greater for wide-body than for single-aisle aircraft. Wide-body planes are used predominantly for long-haul flights where extra space in premium classes has a higher value for passengers (and greater potential for profit extraction through higher fares), compared to shorter-haul flights.

When comparing business-class to coach-class seating, the standard ICAO 2.0 factor appears as reasonably accurate (our average ratios are 1.89 for single-aisle aircraft, and 2.28 for wide-body planes), given no additional adjustments for differential load factors nor for the weight factor in calculating the footprints, as seen from Table 4.1. The standard factor is more off with respect to first class flights, where the average single-aisle space ratio (relative to coach class) is 2.92, and the average wide-body ratio is 4.53.

**Table 4.1: Average Ratio of Space Taken Up by Non-Coach Seats Relative to Coach Seats**

|**Class**<br>**Type**|**Widebody**|**Single**<br>**Aisle**|
|---|---|---|
|Economy<br>Plus|1.24<br>(max 1.75)|1.11<br>(max 1.25)|
|Business|2.28|1.89|
||(max 4.0)|(max 2.0)|
|First|4.53|2.92|
||(max 7.0)|(max 3.3)|

Source: Authors’ calculations Ignoring in the following the distinction between economy and “economy plus” (or “economy extra”) classes, we calculate, in Table 4.2, the fractions of floor space taken up by the respective classes, in wide-body and single-aisle aircraft. Interestingly, for wide-body aircraft the share of passenger floor space taken up by premium classes is about 30 percent; while the share of passengers traveling in premium classes does not much exceed 10 percent.

> 18 ICAO Carbon Emissions Calculator, Version 3.0, page 9.

11
<!-- Start of picture text --> No transfer Social Insurance Social Safety Nets Combination of programs (incl. labor market prog.)<br>2 3 4<br>9 8<br>13 15<br>16<br>17<br>22<br>21<br>7 6 18 22 20<br>7<br>9 12<br>16<br>29<br>76 74<br>67<br>60 59<br>53<br>37<br>Africa Middle East & South Asia East Asia and Latin America Europe & World<br>North Africa Pacific & Caribbean Central Asia<br><!-- End of picture text --> Source: World Bank SP Atlas of Social Protection (ASPIRE) _– see Annex Table 3 for countries used for the calculations; country populations used as weights to expand to regions’ average_ How adequate are social protection transfers? Do they provide sufficient resources to the recipients to alleviate their poverty and avoid detrimental coping strategies? ASPIRE data can be used to compare average transfers received with the average consumption of its beneficiaries. Here also the variation across countries is large (Annex 3). Countries in Eastern Europe and Central Asia tend to provide generous support averaging 53 percent (that is, programs cover over one half of the consumption of people who receive it), while countries in Latin America seem to be calibrating their benefit systems to the scale of poverty gap and transfer on average 27 percent to the income of beneficiaries (the average poor person in these countries is about 25 percent below the poverty line). In Africa and developing Asia extreme values can be found, but for most countries with nascent systems it is very low. The lowest adequacy (less than 10 percent) regionally is South Asia and the Middle East and Northern Africa.

To sum up, most countries in the world use multiple social protection instruments, but their simple presence is not yet a sign of their effectiveness. The scale and coverage of social protection differ a great deal across countries. Hence their real impact on poverty and inequality is also likely to vary a lot.

# <u>3.1 The Poverty Impact of Social Protection Programs</u>

How can the impact of social protection on poverty and inequality be estimated? As argued earlier, social protection affects well-being through three channels of impact: first, direct reduction of income poverty through transfer of purchasing power to the beneficiaries, but also two other less direct channels, namely insurance/protection against risk or shocks (where the longer-term poverty consequences of a shock often decrease the "lifetime" poverty of the beneficiaries allowing them to recover better from these shocks); and “investment income” as additional returns or income from productive investment, and employment generated through the participation in social protection programs. Obviously, these three channels are co-existent, and any social protection program has effects on poverty and inequality along all three channels. For example, income security due to the receipt of regular transfers encourages beneficiaries to invest in higher risk but also higher return activities, or overcome market failures allowing them to invest in their human capital. There is mounting 11
stocks and log capital per worker) are presented. In Panel B, descriptive statistics of the time-varying firm-level control variables are presented, which can control for size (log sales), production technology (log purchase), and trade activities (exporter and importer dummies). In Panel C, descriptive statistics of two key dimensional variables (firm and time) are presented. Note that across all years, about 35% of firms in the data set had foreign affiliates in the period 2013–2016.

# **4 Firm-Level Employment Adjustments**

## **4.1 Preliminary Evidence: Event Study Analysis**

We start by estimating employment trends by year for MNCs and non-MNCs, focusing on the sample in all sectors for the period 2013–2019. We document the average employment evolution before and after the 2017 labor market policy changes, and attempt to detect any differential patterns between the two groups. Specifically, we estimate the following event study regression, first for the MNC group and then for the non-MNC group: where the dependent variable, ln E _it_ , is the log of employment for firm _i_ in year _t_ . Firm fixed effects are captured by _ψi_ , and 1 _{s_ = _t}_ is an indicator variable corresponding to a time indicator that equals 1 if the time (year) is _t_ and 0 otherwise. The sample period is 2013~2019, with 2016 as the reference year. We cluster standard errors at the sector level.

Figure 3 plots the _βs_ coefficients for Korean firms with foreign affiliates (in blue) and Korean firms with no foreign affiliates (in red). We do not detect differential pretrends between the two groups as the lead coefficients up to the year 2016 are all statistically insignificant for both groups. Following the minimum wage announcement in 2017, employment levels among the non-MNC group remained almost the same through the year 2019. On the contrary, employment in the MNC group declined by 1.0% in 2017, 3.4% in 2018, and 4.1% in 2019. These differences suggest that the MNCs may have reduced domestic labor in Korea and replaced those domestic workers with foreign factors of production associated with their subsidiaries in foreign countries.

To further investigate the possible labor substitution channel, we zoom in on the manufacturing sector for the following reasons. First, Korean firms’ outward FDI has been highly concentrated in manufacturing (see section 3 for more details). Second, international production fragmentation and the growth in global value chains occurred predom14
Table 5: Judgement text regressions, gender, full sample

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|0.402<sup>_⇤⇤_</sup>|-0.0948<sup>_⇤⇤_</sup>|0.293|82.45|
||(0.195)|(0.0413)|(0.183)|(85.79)|
|Def. maj. female|-0.108<sup>_⇤_</sup>|0.0389|-0.130|-28.85|
||(0.0650)|(0.0613)|(0.0844)|(22.16)|
|Judge maj. fem. X def. maj. fem.|-0.0790|-0.0437|0.228|-25.34|
||(0.123)|(0.0674)|(0.151)|(46.18)|
|DV mean|2.020|0.250|2.200|1484.0|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|22801|22801|22801|22801|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the full sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Def. = defendant, maj. = majority, fem. = female.

Table 6: Judgement text regressions, gender, defendant win

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|0.326|-0.119<sup>_⇤⇤⇤_</sup>|0.126|85.48|
||(0.205)|(0.0459)|(0.223)|(86.29)|
|Def. maj. female|-0.0684<br>(0.0855)|-0.0653<br>(0.0843)|-0.218<sup>_⇤_</sup><br>(0.129)|56.57<br>(35.86)|
|Judge maj. fem. X def. maj. fem.|-0.176|0.0805|0.186|-140.1<sup>_⇤⇤_</sup>|
||(0.175)|(0.0975)|(0.203)|(67.75)|
|DV mean|2.076|0.257|2.303|1517.9|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|10235|10235|10235|10235|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-win sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Def. = defendant, maj. = majority, fem. = female.

21
Figure 3.13 Promotion rate to tertiary education for graduates of academic senior secondary education <!-- Start of picture text --> 100<br>95 94.5<br>90 90.2 92.5<br>85 86.5 87 87.6<br>80 83.3<br>75 77.6<br>70 72.7<br>70.3<br>65<br>60<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Source : National Bureau of Statistics of China, 2016b.<br>Figure 3.14 Numbers of total university entrants versus entrants from academic senior secondary<br>education<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Entrants to teriary education Graduates of academic upper secondary education<br>Percent<br>10,000 people<br><!-- End of picture text --> Figure 3.14 Numbers of total university entrants versus entrants from academic senior secondary education _Source:_ National Bureau of Statistics of China, 2016b.

While vocational education provides a path for many youths with rural background to upper secondary education, it perpetuates the urban and rural divide. Due to the large gaps in the quality of basic education as well as the costs associated with continuing education, rural children exhibit lower progression to the academic stream of upper secondary schools. The overall enrollment rate for upper secondary education increased from 79.2 percent in 2009 to 87.0 percent in 2015 and much of the increase was due to the expansion in vocational education. In the meantime, the enrollment gap for academic upper secondary schools remains large between urban and rural students. In 2015, 78 percent of urban lower secondary school graduates entered the academic stream of senior secondary education, in contrast to only 45 percent in rural areas (including county and township areas).

33
23 Jelliffe, D.B. The Assessment of the Nutritional Status of the Commuitv with Special rteference to Field Surveys in Developing Regions of the World. World Health Organization, Monograph No. 53, Geneva, 1966.

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# **1 Introduction**

Small businesses are the main source of income for households across Sub-Saharan Africa (outside agriculture) (International Labour Organization, 2019), however, they contribute a very modest fraction of total tax revenue. Many governments have tried to address this by registering large numbers of small businesses (Benhassine _et al._ , 2018, Moore _et al._ , 2018) and using simplified tax regimes (STRs) for registered, small businesses with low revenue levels (Mas-Montserrat _et al._ , 2023). These regimes, often called turnover or presumptive tax regimes, are meant to provide an ‘on-ramp’ for small businesses to meet their tax obligations as they are easier to comply with than more complex corporate or personal income tax regimes.<sup>1</sup> Beyond simplifying the makers aim for these STRs to collect taxpaying process, policy typically meaningful amounts of revenue from small businesses that were otherwise likely to have remained informal. In addition, there is often a desire to ensure that the tax burden is distributed equitably among small businesses and across tax types. Despite the widespread use of STRs, relatively little is known about how well, in practice, these regimes actually meet these objectives of revenue collection, simplicity, and equity.

We present novel empirical insights about the trade-offs that policy makers face when setting the design features of STRs in Sub-Saharan Africa by conducting a comprehensive cross-country review of these regimes and a detailed examination of the turnover tax (TOT) regime in Kenya. We systematically document the specific design features of STRs in each Sub-Saharan African country (when one exists) by reviewing the most recent tax legislation and policy guidance notes. Building on this review, we conduct detailed analysis of ten years of tax administrative data from the TOT regime in Kenya. Kenya provides an insightful case study of the challenges governments face when taxing small businesses, as despite there being more than 1 million potential TOTpayers, the regime contributes less than 1 percent of total tax revenue. In recent

> 1For example, in February 2024 in Kenya, small businesses with an annual turnover between USD7,500 and USD187,500 were only required to calculate their monthly turnover and to pay 3 percent of this amount in tax. The minimum threshold is set to align with the personal income tax exemption threshold.

1
_percentages of how much weight you placed on each criterion. (Please make sure it adds up to 100%!) – [Growth opportunity, Investment opportunity, Improvement made during program]._ Given that investors do not accurately explain the criteria that are important to them (Petty and Gruber 2011), we followed recent field experiment research and conducted semi-structured interviews to provide more insight into mechanisms driving results (e.g., Dimitriadis and Koning 2022). The first author interviewed all 15 trainee investors in one region to further examine how they decided to give a startup a high or low score – what investors were paying attention to during evaluation.

To assess the effect of systematizing inquiry on individual investors’ evaluation, we compared how investors in the treatment and control group evaluated startups using a set of criteria, by founder gender, across three phases of evaluation. We used the same regression model, but changed the dependent variable to each criterion score i.e., score for “business model”. We then reran the regression to assess how investors re-evaluated startups from their first impressions at baseline.

**6.5. Results:** The balanced panel dataset after attrition was 1,530 investment decisions made by 65 investors on 69 startups over three time periods (510 decisions per period). Attrition was largely due to Covid-related absences. As shown in Table 4, there was no significant difference in observable characteristics between treatment and control groups – using raw numbers, percentages, or the p- value taken when regressing each characteristic on treatment.

To assess the baseline, we used trainee investors in the control group. Similarly to Stage 1, investors asked startups represented by females more prevention-focused questions and awarded lower scores than startups represented by all males. However, during the program, we observed this effect lessening, observing only directionally different effects by gender (see Appendix D). This suggests that the Vilcap program itself reduced gender disparities in the control group. The “Vilcap effect” could be driven either by the fact that all startups continue to be evaluated over time, or Vilcap 25
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# **1.** Introduction

Governments in developing countries spend an average of **26** percent of **GDP** on goods and services, a figure which has risen **by** eight percentage points over the last fifteen years (World Bank **[1991]).** The magnitude and growth of this figure has prompted a fair amount of research on the relationship between the size of government and economic growth'. Much less is known about how the _composition_ of public expenditure affects a country's growth rate?. For at least three reasons, this question is becoming increasingly important. First, after a decade of fiscal adjustment, during which many of the "white elephants" in government budgets were weeded out, some developing countries are faced with hard choices when undertaking further fiscal restraint. Which component of public expenditure should be cut? Health? Education? Infrastructure? Defense? Before taking a decision, policymakers need to know the relative contributions of these different components to the country's economic performance. Second, the World Bank periodically undertakes Public Expenditure Reviews of its client countries where, among other things, the mix of public expenditures is evaluated. **A** systematic analysis of how this mix affects economic growth would lend much-needed support to these evaluations. Third, reviews of the experience with structural adjustment have shown that adjusting countries have followed a different composition of public expenditure from non-adjusting countries (see World Bank **[1992]).** What are the implications of this difference for the future prospects of these countries? The purpose of this paper, therefore, is to shed light on the relationship

> 'For a survey, see Lindauer and Velenchik **[19911.**

> Me only systematic study is Diamond **[19891.**

1