rafmacalaba/datause-encoder-data huggingface dataset:
|Road Sector Development Project|Sri Kumar Tadimalla|
|---|---|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Road Sector Development Project|Sri Kumar Tadimalla|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|Nepal - Village Micro Hydro|William Nicholas Bowden|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|ElectricityDistribution and Transmission Improvement Project|Mohammad Saqib|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|LAC Regional Sustainable Transport and AirQualityProject|Anca Cristina Dumitrescu|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
|Assam State Roads Project|Tesfamichael NahusenayMitiku|
Since the wage subsidy is given to employers of formal wage workers, we describe briefly the characteristics of the formal labor market from the LFS (2000) and IES (2000) surveys. Figure 2 shows formal employment by skill level. High-skilled formal workers<sup>8</sup> constitute 24 percent of the total formal work force. Semi-skilled workers<sup>9</sup> constitute 55 percent of the total formal employment and low-skilled workers that are defined as elementary occupations<sup>10</sup> constitute about 21 percent of the formal wage labor market. Overall about 72 percent of formal employment is characterized by either low- or semi-skilled workers.

Formal wage workers in agricultural and retail trade sectors are relatively poor. Based on income thresholds from a recent study on South Africa (Altman, 2007), formal workers can be classified into three groups: i) very poor, ii) working poor, and iii) not poor. The working poor refers to anyone who is employed by the definition of the South African Labor force survey (also in line with the International Labor Organization (ILO) definition), working and earning less than R2,500 per month. This threshold is close to that chosen by National Treasury as the minimum level below which workers are exempt from income tax.<sup>11</sup> Table 4 reflects only formal employment by economic sectors. Agricultural and trade sectors hold the largest share of very poor or poor workers. Manufacturing sectors also have relatively large shares of working poor formal workers (18 percent). On the other hand, non-poor workers are mainly employed in services although manufacturing and financial sectors also have significant shares of non-poor workers, with 19 and 14 percent respectively.

<!-- Start of picture text --> Figure 3: Poverty Profile of Formal Wage Figure 4: Earnings by Gender<br>Workers by Skill Types (2000 prices)<br>Formal Wage Workers by Skill Types Average Earnings by Gender Groups<br>90% 50,000<br>80% 45,000<br>70% 40,000<br>35,000<br>60%<br>30,000<br>50%40% Working Ver PoorWorking PoorWorking Non-Poor 25,000 FemaleMale<br>20,000<br>30%<br>15,000<br>20%<br>10,000<br>10% 5,000<br>0% Low Skill Med Skill High Skill - Formal Wage Informal Wage Self-employment<br>Skill Level Labor Types<br>Source: Authors’ calculations from LFS 2000. Source: Authors’ calculations from LFS 2000.<br>Proportion of formal workers Average Annual Earnings(2000)<br><!-- End of picture text -->

- 8 High skilled formal workers include legislators, senior officials, professionals, technical and associate professionals.

> 9 Semi skilled workers are: clerks, service workers and shop and market sales workers, skilled agricultural and fishery workers, craft and related trades workers, plant and machine operators and assemblers. 10 Low-skilled workers include elementary jobs.

11 The minimum level of annual income subject to income tax was R32,000 in 2004. We converted this value in 2000 prices using CPI to make it comparable to our study. In 2000 prices, the minimum annual income would be about R25, 848.

- 11 -
cohort are those who completed age 14 in 1998. We use the data from the PNAD 1998 to perform the estimates.

The second placebo test uses data from 2001 and compares boys who turned 14 before and after December 2000, two years after the change in the law. The third placebo inspects the existence of discontinuities in the outcomes of interest at the cutoff of first week of July 1999. The two cohorts examined are the males that completed 14 years in the first half of 1999 and the ones that completed 14 years in the second half of 1999. The treatment dummy is equal to 1 for those that completed 14 in the second half of 1999. We use the data from the PNAD 1999. This analysis compares two cohorts affected by the ban.

Table 4 shows the results of the three placebo tests with piecewise linear specification. Except for one outcome in 2001, the results suggest no discontinuities in the outcomes variables.

# **6.2. Persistence of Short-Term Effects**

This sub-section explores the persistence of the ban effect along the school ages. Specifically, we look at the ban effect comparing the outcomes of ‘treated’ and ‘control’ cohorts between 1998 and 2006.<sup>27</sup> We then follow the ‘treated’ cohort between ages 13 and 21. We start with 1998 so that we have additional robustness checks since we look at the affected and unaffected cohorts one year prior to the change in the law.

Figure 4 shows the time evolution of school attendance rates, and the work incidence in paid and unpaid work for the unaffected cohort of boys. School attendance declines steadily from about 94 percent at age 13 to around 27 percent at age 21. In contrast, paid work incidence increases steadily from 8.4 percent at age 13 to 71 percent at age 21. The unpaid work incidence decreases steadily as boys get older.

Figure 5 presents the evolution of the six different combinations between schooling and work activities for the unaffected cohort. About 82 percent of 13-year-old boys were only studying. That number drops to about 11 percent by the time they reach age 21. As expected, the proportion of those doing paid work only increased over time, reaching 55 percent by the time individuals were age 21. Interestingly, about 20 percent

> 27 There is no PNAD in the year 2000.

21
**Figure 3. Intangible residual share of total wealth, 2005**

<!-- Start of picture text --> 30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40%<br><!-- End of picture text -->

# Source: Authors’ calculations

For the countries analyzed, the unweighted average share of human capital in total wealth (excluding Korea, which is clearly an outlier) is nearly 62%, and, as Figure 2 shows, the shares fall in the range of 54% to 74%. These shares can be compared with those for produced capital, which average roughly 17% across the countries in our sample,<sup>12</sup> and those for natural capital, which average 4.5% of total wealth (compared to 2% for high income countries in total as seen in Table 1).

Turning to the intangible residual, this is in effect computed as a ‘residual of a residual.’ Intangible capital, as measured in World Bank (2011), is measured as the difference between total wealth and tangible capital, both produced and natural. The estimate of the intangible residual shown in this paper is instead computed as the difference between the World Bank measure of intangible capital and the OECD human capital measured using the lifetime income approach.

Since estimated total wealth depends upon the assumption of constant returns to scale (CRS), the calculated intangible residual implicitly includes intangible assets such as institutional quality and social capital, missing tangible assets (e.g. fish stocks, owing to a lack of data), as well as any deviations from CRS and measurement errors.

As Figure 3 shows, the share of the intangible residual in total wealth exhibits considerable variation across countries. For a cluster of the highest income countries (the United States, Italy, the United Kingdom, France, the Netherlands), however, the share stands close to 25%. Excluding Korea, the unweighted average share of the intangible residual in total wealth is nearly 18%.

12. One reviewer has pointed out that there is a high correlation between the value of human capital and the value of produced capital in Table 3. This is consistent with a constant elasticity of substitution (CES) production function. Since the cross partial derivatives with respect to factors are positive for a CES production function, employing an additional marginal quantity of produced capital will increase the marginal return to human capital.

12
household panel survey), it is interesting to note that the micro estimates of mobility fall mostly within the bounds based on macro data (although more towards the upper half of the range). It is also useful to interpret the movements over time in the upper bound on the mobility parameter. Recall that this upper ��� **�** ����� �� � � bound is ���� = ��+ ����� �� **�** and moves inversely with the variance of log income, ���� . For example, the decline in ��������� since 1980, i.e. the increase in our upper-bound estimate of mobility, is driven � entirely by the increase in overall inequality during this period, i.e. the increase in ���� . The rationale for � this is straightforward. Given our identifying assumption that � and �� are constant over time, we interpret the increase in overall inequality as reflecting an increase over time in the variance of the � idiosyncratic shock, ��� . Since these shocks are independent over time, they have only transitory effects on income, and as a result mobility is higher.

# **6.  Application:  Anonymous and Non-Anonymous Growth in a Cross-Section of Countries**

Popular discussions of trends in inequality frequently refer to income growth rates of “the rich” (defined as the top X% of the income distribution) and income growth rates of “the poor” (defined as the bottom Y% of the income distribution). One prominent example is the World Bank’s declared goal of promoting “shared prosperity”, defined as growth in the bottom 40 percent of the income distribution. Similarly, a body of work by Thomas Piketty and his collaborators has drawn widespread attention to trends in “top incomes”, defined as average incomes in the upper percentiles of the income distribution.

Measuring and interpreting the income growth rates of such population subgroups is complicated by the basic data problem that motivates this paper: true panel data tracking individuals over time is rarely available. Absent such panel data, group-average growth rates typically are calculated based on repeated cross sections. For example, the growth rate of “the poor” would be calculated by comparing average incomes in the bottom Y% at two points in time. Since growth rates calculated in this way do not track individuals over time, they are referred to as “anonymous” growth rates. In contrast, “nonanonymous” growth rates track the same individuals in an initial reference group over time, and can be very different from their “anonymous” counterparts when there is mobility in the income distribution.<sup>20</sup>

> 20 We are by no means the first to notice this distinction -- see Jenkins and Van Kerm (2006), Grimm (2007), Van Kerm (2009), and Bourguignon (2011) for discussions of the difference between anonymous growth incidence curves and their non-anonymous counterparts. The novelty in this section of our paper is that we are able to compute estimates of the difference between anonymous and non-anonymous growth rates for a large sample of countries, using our estimates of mobility based only on aggregate moments of the income distribution.

19
_without_ the tax (or transfer).<sup>8</sup> Table 4 shows both the Kakwani progressivity index for each tax and transfer along with its marginal contribution to reducing inequality and poverty for each tax and transfer intervention in 2014 under the main scenario. Results for the alternative scenario where old age pensions are considered as transfers are presented in Appendix 3. We describe each of these in turn.

# **Taxes**

Direct taxes and social contributions are progressive and inequality-reducing overall, however they also place a burden on the poor. That they are progressive can be seen by the fact that the Kakwani coefficient is positive, the fact that they are inequality-reducing can be seen by a positive marginal contribution to the redistributive effort (Table 4). However, they are also poverty increasing, as shown by a negative marginal contribution to poverty reduction when poverty is measured using US$5 PPP-a-day poverty line. There is substantial heterogeneity across categories of taxes and contributions. For instance, while PIT is progressive and redistributive, direct taxes on agriculture/fishing/hunting are slightly regressive, potentially because these taxes could be overestimated by respondents in household surveys from where we take the information. Similarly, social contributions to pensioner’s health and farmer’s contributions are regressive, but small enough that they do not make a large impact on poverty or inequality. In particular, we find that direct taxes in 2014 were not just progressive, but also highly redistributive relative to other countries (Figure 7A).

In contrast, indirect taxes were regressive and contributed to increasing poverty and inequality in 2014. While it is true that regressive taxes can be equalizing,<sup>9</sup> this was not the case in Croatia in 2014. In particular, VAT placed a large burden on low-income households, which was not compensated for by pro-poor spending, leading to an overall increase in poverty and inequality. Excise taxes were also regressive, but since they are not as large as VAT, their impact on poverty and inequality was substantially smaller (Table 4). When taken together, indirect taxes are not only regressive, but also highly unequalizing relative to other countries, including many Latin American and other developing countries (Figure 7B). These results point to potential improvements that could be achieved to reduce the burden on the poor.

> 8 Note that there is path dependence in estimating these marginal contributions, since the order in which each intervention is considered matters for the size of the estimated marginal contribution. The estimation approach uses a Shapely decomposition to address this issue, which involves estimating marginal contributions in every possible path and then taking the average.

> 9 For instance, the case of Chile. See Martinez-Aguilar et al (2017) and Lustig (2017).

19
Next, we conduct a series of alternative analyses to check robustness and enrich the interpretation of our basic results. First, to fully correct for differences in time invariant and 1993 characteristics between districts, we match samples of producing and non-producing districts by means of a propensity score, and test for differences in socioeconomic outcomes using a kernel matching estimator. The results are almost the same as in the benchmark specification. Second, we test the extent to which the magnitude (and not only the presence) of mining activity, measured by the log of the value of mineral production, affects socioeconomic outcomes. We find that larger mines have a stronger impact, both on welfare and inequalities.

Third, to investigate further the geographic impact of mining activity, we refine the spatial analysis and consider the _geographic_ proximity of districts, as an alternative to the _administrative_ (provincial) proximity in the benchmark specification. We use mapping software to identify direct neighbors of mining districts, as well as their second neighbors. Our findings remain basically unaffected: the positive impacts of mining activity decrease rapidly with (geographic) distance, while income inequality increases in both producing and non-producing districts.

Fourth, the localized impact of mining activity calls for an investigation of the impacts of mining revenues (i.e. the _Canon Minero_ ) that, to varying degrees, are distributed to all districts in mining departments (the highest subnational administrative level). Because producing districts also receive a higher share of the _Canon_ , the specifications described up so far do not distinguish between the impacts from direct mining activity and the effects associated with higher local government revenues through the _Canon_ . To overcome this issue, we exploit exogenous variation in the allocation rule of the _Canon_ between producing districts, districts in producing provinces, and in non-producing provinces. Then, we control for socioeconomic characteristics that may affect the allocation of the _Canon_ by instrumenting each district’s mining revenues with the value of mining production at the district, province, and department levels. Once department fixed effects are accounted for, we do not find any significant impact from the _Canon_ . This lack of impact is in line with some of the findings from oil extraction studies (Caselli and Michaels, 2009), and calls into question the usefulness of local revenue sharing agreements without accompanying them with strong monitoring and capacity building efforts (Bardhan and Mookherjee, 2006; Loayza, Rigolini and Calvo-Gonzalez, 2011).

Our findings add to an emerging literature that investigates the local impacts of commodities’ extraction. How natural resources affect living standards has been subject to wide but inconclusive investigations. Early cross-country studies based on cross-sectional analyses (Sachs and Warner, 1995 and 2001) tend to find a negative association between natural resource abundance and economic 5
|**Category**|**Sub-**<br>**category**|**Subcategory name**|**SECTOR/Common**|**Label**|**Answer**|**Score**|
|---|---|---|---|---|---|---|
|B- Operations|B2|Conditions on services<br>suppliers|Common|Managers must be nationals|yes|0.25|
|B- Operations|B2|Conditions on services<br>suppliers|Common|National employees: Min. percentage required|yes|0.25|
|B- Operations|B2|Conditions on services<br>suppliers|Common|International<br>data<br>transfer/access<br>constrained<br>or<br>prohibited|yes|0.25|
|B- Operations|B2|Conditions on services<br>suppliers|Common|Foreign suppliers treated less favourably regarding taxes<br>and/or eligibility for subsidies|yes|0.25|
|B- Operations|B2|Conditions on services<br>suppliers|Horizontal|International<br>data<br>transfer:<br>distinction<br>between<br>economies/regions|yes|0.25|
|B- Operations|B2|Conditions on services<br>suppliers|Professional|Hiring of locally-licensed professionals as employees<br>prohibited|yes|0.25|
|B- Operations|B2|Conditions on services<br>suppliers|Telecom|Ownership and operation of an international gateway<br>permitted|no|0.25|
|B- Operations|B2|Conditions on supply<br>of services|Financial|Foreign branches allowed to take all types of deposit-<br>taking and lending|no|0.25|
|B- Operations|B2|Conditions on supply<br>of services|Financial|Foreign bank "branches" subject to endowment capital<br>requirement|yes|0.25|
|B- Operations|B3|Conditions<br>on<br>government<br>procurement|Transport|Quota for government cargo: Bulk|yes|0.125|
|B- Operations|B3|Conditions<br>on<br>government<br>procurement|Transport|Quota for government cargo: Liner|yes|0.125|
|B- Operations|B4|Conditions on supply<br>of services|Horizontal|Limits on subsequent transfer of capital and investments|yes|0.25|
|C- Competition|C1|Conditions on conduct<br>of firms|Transport|Carrier agreements exempt from competition law|yes|0.25|
|C- Competition|C1|Conditions on conduct<br>of firms|Telecom|Unbundling of the local loop required|no|0.25|
|C- Competition|C3|Other measures|Transport|Industry representatives involved in setting entry and<br>pricing regulations|yes|0.125|
|C- Competition|C3|Other measures|Telecom|Interconnection mandated|no|0.25|

57