rafmacalaba/datause-encoder-data huggingface dataset:
# **6. New Inequality Insights and an Agenda for the Future**

We conclude by stressing three key aspects of inequality that ancient preindustrial experience has uncovered. First, as measured by the Gini coefficient, income inequality in still-pre-industrial countries today is not very different from inequality in distant pre-industrial times. In addition, the variance between countries then and now is much greater than the variance in average inequality between then and now. Second, the _extraction ratio_ – how much of potential inequality was converted into actual inequality – was significantly bigger then than now. We are persuaded that much more can be learned about inequality in the past _and_ the present by looking at the _extraction ratio_ rather than just at actual inequality. The ratio shows how powerful and extortionary are the elite, its institutions, and its policies. For example, in a regression using ancient inequality evidence (not included in the text) a dummy variable for colony has a strong positive impact on the _extraction ratio_ . Furthermore, while a relation between conflict and actual inequality has proven hard to document on modern evidence (see Collier and Hoeffler, 2004), we conjecture that the introduction of the _inequality possibility frontier_ and the _extraction ratio_ might shed brighter light on that hypothesis. Third, differences in lifetime survival rates between rich and poor countries and between rich and poor individuals within countries were much higher two centuries ago than they are now, and this served to make for greater lifetime inequality in the past. Fourth, unlike the findings regarding the evolution of the 20<sup>th</sup> century inequality in advanced economies, our ancient inequality sample does not reveal any significant correlation between the income share of the top 1 28
owned rms: where forjt is a dummy variable if fi rm j is foreign owned. The fi xed effect, ηkj, summarizes the impact of permanent differences in observed and unobserved characteristics affecting the outcome ykjt. The remaining variables have the same notation as above<sup>28</sup> . Finally, the error term, �kjt, is assumed to be uncorrelated across fi rms and time. The parameters in equation (3.2) are estimated, including the fi xed effects, by least squares. Thus, no matter how the fi rm’s permanent characteristics are related to the acquisition, the estimates of this effect are unbiased.

This estimation approach generalizes the differences in differences technique. The need for including another group of fi rms in the regression is made clear in Meyer (1994). Comparing an outcome before and after the acquisition is not sufficient because it could also be affected by other factors that are contemporaneous with the acquisition. Assuming that shocks contemporaneous to the acquisition affect acquired fi rms and control fi rms in the same way, the coefficient on the foreign ownership variable would be an unbiased estimator of the impact of ownership change. The main problem of applying this research design in this sample is that it is not appropriate when the two groups being compared are very different already during the pre-acquisition period. As suggested by the fi ndings in table 1, acquired and foreign fi rms are very similar with respect to their workforce composition. Therefore, to analyze changes in labor market outcomes following the foreign acquisition, I start by comparing acquired fi rms with other foreign-owned fi rms, as these are a better counterfactual than domestic fi rms. The important issue is to establish what would have happened to the fi rm had it not been acquired by foreigners. By using this group it is less likely that estimates are biased due to the selection.

Column (3) in tables 2 and 3 present the results of estimating equation (3.2) using foreign fi rms as control group. Following the acquisition, there are no signi fi cant changes

> 28Region and sector dummies are identi fi ed because there are switchers in these categories. However, empirical fi ndings do not depend upon their inclusion.

13
using alternative measures of reliance on external or informal financial sources. These are indicator variables taking value 1 if firms report obtaining financing for their working capital or new investment from external sources such as banks, leasing arrangements, and credit cards; or informal sources such as family loans, money lenders, and trade credit. Tables 1-5 report sample statistics.

# **IV. Some stylized facts**

In this section, we briefly describe how our measure of formality covaries in the aggregate with income and other measures of the size of the informal sector that have been used in the literature. In particular, we compare the average country percentage of sales not reported for tax purpose with Schneider’s (2005) estimates of the shadow economy, and Loayza and Rigolini’s (2006) self-employment ratio.

Simple cross-country correlations indicate a moderate positive correlation between FORMALITY_AVG and (log) per capita income (0.29), and a low correlation with Schneider’s (-0.12) and Loayza and Rigolini’s (-0.06) estimates of the shadow economy.<sup>10</sup> The partial overlap between tax compliance and the existing measures of informality reflects different measurement methodologies as well as different angles of the concept of informality. In particular, unlike estimated informality from money demand equation or MIMIC models, tax compliance is a direct measure of informality. Thus, self-reported tax compliance is probably a more precise measure of a partial concept of informality, as turnover of non-registered (i.e. fully informal) companies or income of self-employed workers is not captured by our measure. As such, our measure of informality is an important complement to the existing ones.

Interestingly, we find little correlation between FORMALITY_AVG and countries’ corporate tax rate, indicating that differences in tax rates are more likely to affect firms’ choices on the margin between formality and informality rather than the extent of tax compliance for registered firms. Not surprisingly, we find that in the aggregate formality is highly correlated with access to credit as measured by the share of firms with a credit line (fig.1-5).

As previously noted, the IC surveys have a bias toward large and mature firms, so that we might be concerned about the representativeness of simple averages. To gauge the extent of the potential gap, we use data on firm distribution by size to compute weighted averages of formality within countries. In particular, we use the percentage of establishments by size class from Kozak

> 10 See also World Bank (2007).

8
Figure 21. Distribution of IESS Contributors per Wages Intervals. Year 2018 <!-- Start of picture text --> 0.45<br>0.40<br>0.35<br>0.30<br>Men Women<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>4.6 6.0 8.4 12.0 24.0 36.0 60.0 72.0<br>Annual Wages (in thousands)<br><!-- End of picture text --> Source: IESS- based own source.

Figure 22 presents the results found in the simulation of the financial flow in the traditional pay-asyou-go scheme given the introduction of a funding pillar. The results suggest that, given the assumptions established in this simulation, the cost of transition to be faced does not seem significant. In effect, the pure financial result remains positive until the beginning of 2030, a point in time at which it turns negative. These almost five years in advance of the negative result relate to the cost of transitioning from one scheme to another. However, in the long term, the savings made in terms of deficit are equal on average to almost 2 GDP points.

31
2014. The household survey covers a range of topics related to household well-being and socioeconomic status, including individuals’ work history and their fertility decisions. Information on household and individual-level characteristics were also available. Given that the original household members in 1993 were tracked when they moved or later got married and started a new household, the number of individuals grow as we move across surveys due to these new households. For our analysis, we kept households who are observed and tracked in all four waves so that we have a good amount of information on their work history.

Individuals who were at least 15 years old at the time of the survey were asked about their current and previous work status. A person is considered to be currently working if they responded yes to one of the following prompts: (1) identified working as the primary activity, (2) had work for pay for at least one hour in the past week, (3) having a job/business but currently is off work, (4) or working for a family-owned farm/business (unpaid family workers). We use this definition of working as our main outcome of analysis. For the years between the surveys, individuals also reported whether they worked that particular year. For each year including the current one, those who worked also answered additional questions about their job, such as the sector of occupation (10 categories of sectors which we simplified into three - agriculture, industry, and service) and the type of occupation (self-employed workers, government or private employee, unpaid family worker, and agricultural/non-agricultural casual workers).

We restricted our sample of analysis to individuals who exclude individuals based on the timing of first childbirth. We first identify the year when the first child was born. Then, individuals with children born outside the 1993-2014 survey period were excluded.<sup>1</sup> We further restricted our sample to parents who had their first child between ages 20 and 40. The prevalence of individuals who had their first birth before 20 and after 40 is quite low. Individuals with incomplete work histories were excluded and the sample only includes respondents with at least 5 years of work history. Our final sample comprises 2,118 mothers and 2,800 fathers.

A critical caveat in our study concerns the non-randomness of never-parents. The sample of never parents consists of 3,085 men and 2,110 women. Because we restrict our sample based on age at first birth, we are not able to apply similar restrictions on the never parents, as the age at

> 1Inconsistencies in age and birth year data required additional data cleaning.

6
generic skills and seeks not to cut off the range of students' options too early, allowing them to move more easily to higher levels of learning in the same field or a new one (Hobart 1999).

The importance of career programs that allow students to develop general skills alongside technical ones has been acknowledged in many countries (Frantz 1998). These skills have different names in different countries-they are called key competencies in Australia, strategy for prosperity in Canada, process independent qualifications in Denmark, crossing or transferable skills in France, key qualifications in Germany, essential skills in New Zealand, core or conmmon skills in the United Kingdom, and workplace know-how in the United States (Hobart 1999). In light of the increased need for more generic skills, Mexico has started to re-examine its own strategy, as specific technical skills can quickly become outdated.

# **V. The Evaluation of the CONALEP System: Students and Graduates**

The socioeconomic and academic level of CONALEP students varies according to location. Data from the National Evaluation **Figure 1** Center _(Centro Nacional de Evaluaci6n,_ **Family Income of Students** at **Selected lnstitutions lnstitutions** in **1999** CENEVAL) suggests that CONALEP most 500-CONLLEP **'S** frequently serves students from a lower 0 **4a0** -uw--Col.Bach. EdlOMeX. socioeconomic status at the upper-secondary = -m **DGETI** school level in Mexico City. The results of a 8 ooo\ random sample of those who took the entrance a **Y/** \eag **0** examination to upper-secondary school in the Frequercy in Populationin Population metropolitan area of Mexico City in 1999 _Note:_ This graph assumes normal population distribution.

<!-- Start of picture text --> Family Income of Students at Selected lnstitutions lnstitutions in<br>1999<br>500-CONLLEP<br>'S<br>0 4a0 -uw--Col.Bach.<br>EdlOMeX.<br>= -m DGETI<br>8 ooo\<br>a Y/ \eag<br>0<br>Frequercy in Populationin Population<br><!-- End of picture text --> 16
Finally, Appendix C notes two minor adjustments to the secondary analysis we had preregistered as AEARCTR-0002482. First, we cannot analyze balance by facility catchment size and health worker education level because the data on catchment size are largely missing (not reported at the facility level) and because there is little variation in health worker education level. We explore tenure and experience in lieu of education and find the arms to be balanced. Second, we reassigned midwives to the medical professional rank. We had intended to include them as lowerlevel professionals, but found that their training, experience, and salary are comparable to that of a nurse. The results are robust to their inclusion in lower-level ranks instead.

# **4 Setting and data**

In this section, we describe the study setting and data collection as well as the outcome measures, key covariates, effect moderators, and potential confounders.

## **4.1 PFP trial and impact evaluation**

We embedded the experiment in the endline survey of a concurrent cluster-randomized trial of different health facility financing modalities in Nigeria (Kandpal et al., 2019). This trial randomized all 52 districts in three states to two arms. A total of 1,389 primary and secondary care facilities were either assigned (1) to PFP with quarterly bonuses based on the quantity and quality of primary health services they provided or (2) to direct facility financing (DFF) that disbursed half of the average PFP bonus without conditioning the payment on performance. In both arms, district supervisors administered a checklist to assess quality of care on a quarterly basis; an independent agency verified performance in the PFP facilities. A “business as usual” control group was established by selecting three states in the same geopolitical zone that border the intervention states and resemble them along observed demographic dimensions that are associated with primary health care outcomes. Appendix Figure A.1 shows the intervention states (Adamawa, Nasarawa, Ondo) and the control states (Taraba, Benue, Ogun) as well as the locations of the health facilities in this study.

13
Survey data also reveal that taxpayers’ perceptions of corruption vary, as a result of the reforms.<sup>36</sup> Indeed, the differences are striking. In Peru 73% of respondents classified the agency’s corruption level as _Fairly_ or _Very Low_ , as opposed to 20% for Mexico and 6% for Venezuela. In contrast, 29% in Venezuela classify corruption as _Fairly_ or _Very High_ , as compared with 17% for Mexico and 2% for Peru.

In terms of the impact of the reform on corruption, Peru again surpassed the others, with 85% responding that there was _Substantially_ or _Much Less_ corruption than before the reform, as compared with only 26% in Venezuela and 21% in Mexico. The relatively smaller impact in Venezuela reflects the perception of backsliding since the reform, whereas the value for Mexico reflected the perception of the of reforms’ minor impacts.

Of the African cases, Kenya and South Africa have gone farther than Uganda. Uganda, however, was not without some progress in this area. It is thought that the URA reform has reduced the amount of corruption in the tax agency, as compared with the prereform agency, and has reduced the impunity of the corrupt officials that remain. The combined effect of higher salaries and stiffer penalties for malfeasance vis-à-vis public service employment conditions seems to have made an impact. The URA reform is reported to have led to less moonlighting. The URA recently established an on-line recruitment system, in which specific posts are advertised against the establishment register and application processing is handled electronically.

Very encouraging is the perception in Kenya, held by both KRA officials and private sector tax experts, that corruption has decreased since the reform. According to one professional tax accountant, “I’m less likely to get asked for a bribe than in the old days.”<sup>37</sup> However, as would be expected, significant corruption still exists, especially in customs. According to a previous Commissioner General (CG), “elaborate schemes of fraud exist.”<sup>38</sup> Yet in order to reduce corruption further, additional resources and perhaps additional autonomy, will be required.

> 36 Survey data from Taliercio (2000).

> 37 Interview, Nairobi, Kenya, 7/6/2001.

> 38 Interview, Nairobi, Kenya, 7/4/2001.

26
However, the elasticity of GDP with respect to roads is higher in poor countries (0.29) than in medium income countries (0.15), which in turn is higher than in high-income level countries (-2.6). One possible reason could lie in the definition of the variable as kilometers of paved roads per capita. This means that roads that are not paved (see the Appendix for the precise meaning of “paved”) are not taken into account. Poor countries have the lowest volume of paved roads per capita. In these countries with a limited supply of paved roads, the marginal contribution of a km of paved road to growth can be expected to be higher than in countries with a denser network of paved roads. Additionally, as discussed in the interpretation of the Governance model (Table 5), this result could perhaps be explained by an omitted variable bias: the widespread use of alternative modes of transportation such as air transport as countries’ income level rises is not considered in our analysis and such alternative modes could potentially decrease the sensitivity of GDP to roads infrastructure in richer countries. Finally, while electricity is associated with a positive and significant influence on growth in East Asia, its impact does not differ by countries’ income levels.

**Table 7: Augmented Solow model with infrastructure variables and the effect of the income level: low, medium or high-income countries**

||Tele<br>|coms<br>|Elec<br>|tricity<br>|Ro<br>|ads<br>|Sani<br>|tation<br>|W<br>|ater<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|
|Education|1.24|−.17|2.49|.00|.73|−. 09|1.23|−.10|1.02|.09|
|t-stat|3.65|−1.18|4.14|−. 01|1.40|−.39|2.73|.07|2.21|.38|
|Elast(β)|1.24|.00|2.49|.00|.73|.00|1.23|.00|1.02|.00|
|Total Invst|.03|−.40|.36|−. 22|.72|.12|−. 39|.02|−.11|.03|
|t-stat|.08|−2.73|.61|−1.01|1.40|.46|−. 65|.07|−.16|.11|
|Elast(α)|.00|−.67|.00|.00|.00|.00|.00|.00|.00|.00|
|Low|||||||||||
|Infrast||.37||.89||.29||.68||−.12|
|t-stat||4.15||4.07||2.47||2.50||−. 21|
|Elast (γ)||.62||.89||.29||.68||.00|
|Medium|||||||||||
|Infrast||.54||.89||.15||.86||.23|
|t-stat||4.29||4.07||−3.96||3.00||3.60|
|Elast (γ)||.90||.89||.15||.86||.23|
|High<br>|||||||||||
|Infrast||.91||.89||−.26||1.46||.94|
|t-stat||7.80||4.07||−8.39||7.58||8.53|
|Elast (γ)<br>||1.52||.89||−.26||1.46||.94|
|R <sup>2</sup>|.29|.93|.41|.94|.24|.84|.20|.88|.14|.85|
|No. Obs|42|42|23|23|28|28|23|23|25|25|
|Difference||YES||NO||YES||YES||YES|

23
This paper seeks to assess how fuel subsidies to the Chinese distant water fleet, by far the largest in the world, have contributed to the steady depletion of global fish stocks. To this end, we use real-time tracking data on the fishing activity of more than 1,000 individual Chinese distant water fishing vessels over the period 2016 to 2020. We identify the effect of fuel subsidies on fishing behavior by exploiting China’s 2016 distant water fuel subsidy reform. This reform created 25 separate thresholds at which fuel subsidies discontinuously change for otherwise similar-sized vessels. The resulting regression discontinuity estimates are large: we find that a 1% increase in fuel subsidies increases hours of fishing by 2.2% and distance traveled by 1.7%. The local average treatment effect indicates that a $10,000 increase in fuel subsidy around discontinuity thresholds leads to 170 additional hours of fishing per month.

We then use these empirical estimates in a counterfactual analysis that isolates the contribution of Chinese distant water fuel subsidies to the overfishing status of global stocks. We find that reforming fuel subsidies to China’s distant water fishing fleet could reduce, but not entirely eliminate, overfishing in most FAO Ocean Regions. Furthermore, a 50% reduction in distant water fuel subsidies could lead to an average gain of 17% in steady state fish biomass across regions, indicating that reforming fuel subsidies is a promising strategy for rebuilding depleted fish stocks.

The results of this paper have key implications for ongoing debates about how to reform subsidies in fisheries. For example, in June 2022, the WTO adopted the “Agreement on Fisheries Subsidies”, which prohibits signatory countries from subsidizing vessels that fish for depleted stocks or engage in Illegal, Unauthorized, and Unregulated (IUU) fishing (WTO, 2022). This agreement is the second ever reached, on any subject, at the WTO. China has already updated its distant water fuel subsidy to comply with the WTO agreement (Ministry of Agriculture and Rural Affairs and Ministry of Finance, 2021a).

While historic, the WTO agreement is narrow in the types of subsidies it limits. Data do not usually exist to demonstrate that stocks in low- and middle-income countries are depleted, and IUU fishing is only one contributor to fisheries depletion. Depletion depends on the total quantity of fishing, not the fraction of fishing that is legal. By demonstrating the large effect of fuel subsidies on the total quantity of fishing, our findings imply that more 40
terms in Tables A.21-A.23 lose statistical significance at conventional levels when the specification further includes interaction terms between the treatment status and all the baseline variables listed in the balance table (Table A.2). The discussion below focuses on the four interaction terms that are robust to the inclusion of these supplementary interaction terms. Second, Tables A.21-A.23 study 140 interaction terms, 14 of which are statistically significant at conventional thresholds. Very few of the terms would remain statistically significant if we were to implement adjustments for multiple testing.

Results suggest that women with an IGA at baseline were less likely to invest in livestock following their participation to the cash grant and training program. Women with an IGA at baseline were also more likely to face other shocks (disease or death of a household member) if they were in the treatment group. We find evidence that the positive effect on the number of other household members with an IGA is driven by households that had below-median consumption levels at baseline as well as households that participated to the CWLP program. Other results are not robust to the inclusion of interaction terms between the treatment status and all the baseline variables listed in the balance table.

We also studied heterogeneous treatment effects by predicted outcomes, using the repeat split-sample (RSS) endogenous stratification procedure of Abadie et al. (2018). Results are reported in Tables A.24 to A.26. Very few results are statistically significant at conventional thresholds, suggesting that treatment effects do not strongly depend on predicted outcomes. We note two exceptions. First, the estimated treatment effect on women’s agency is negative and statistically significant for women with high predicted levels of agency, while the same treatment effect is low and statistically insignificant for women with low or intermediate predicted levels of agency. Second, the estimated treatment effect on the number of other household members with an IGA seems to be driven by women living in households with intermediate or high predicted number of other household members with an IGA.

64