rafmacalaba/data-use-mentions-extended huggingface dataset:
A document from TecnoServe<sup>24</sup> argues that the country has the climate and altitude to produce specialty Arabica coffee (a type of quality that can be sold at premium world market prices). Before 2004, the country farmers used to sell their high quality but poorly processed coffee on the blended coffee market through a state-run auction: a system that did not reward quality. When the country started to receive assistance by several donors, a policy reform allowed a lowering of the farm-gate taxation and permitted producers to sell their product directly to foreign buyers. These reforms, together with technical assistance to the farmers, allowed a change in the sector from 2004 onwards such that the price of coffee is no longer determined exclusively on organized exchanges.

The case of coffee shows that the result in Table 8 could indicate that Tanzanian firms might charge different prices even in homogeneous products, like coffee. In fact when the regression is restricted to Coffee and Tea (HS2 code 9), the result is positive (even though weakly significant). This preliminary result might highlight that countries with a comparative advantage in agricultural goods might still be able to improve the competitiveness of their products and increase the quality and value associated to them, even if they are classified as homogeneous goods. In fact, the 2009 Tanzania National Competitiveness Assessment reported that tea and coffee were two of Tanzania’s most competitive exports in the 20032007 period.

An alternative explanation to the result that price differentiation is only significant for homogenous goods is that firms charge different mark-ups in different markets, rather than firms selling different qualities in different markets. A way to differentiate between “quality led” and “mark-up led” pricing-to-market would be to use an index of vertical differentiation, like the one built by Khandelwal (2010). The problem with this and other indices of vertical differentiation is that they do not include the products defined as homogenous by Rauch. As a consequence, further research is needed in order to establish whether price dispersion across destinations in
# **Introduction**

Biodiversity conservation is essential for sustainable development, poverty reduction, and a healthy planet. It supports ecosystems that are fundamental to human livelihoods by providing food, clean water, and climate stability. Healthy ecosystems ensure the availability of natural resources critical to agriculture, fishing, and forestry, helping to alleviate poverty and promote sustainable economic growth. Additionally, biodiversity preservation supports industries like ecotourism and pharmaceuticals, which generate revenue and create jobs, further contributing to shared prosperity. Maintaining biodiversity is also a key to a livable planet, as it strengthens the resilience of ecosystems that regulate the climate by sequestering carbon and mitigating the impacts of climate change. Ecosystems such as forests, wetlands, and oceans act as natural buffers, stabilizing global temperatures and sustaining life on Earth. Thus, investing in biodiversity conservation is crucial not only for environmental health, but also for human well-being and global sustainability.

However, biodiversity is declining at an alarming rate. A recent UN report by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services revealed that 1 million species are near extinction, with the rate of extinctions accelerating despite increasing awareness of the interconnectedness between human life and nature (IPBES 2019). This report, based on a systematic review of approximately 15,000 scientific and governmental sources, aligns with the findings of the Living Planet Index,<sup>1</sup> which has recorded a 68% decline in global biodiversity since 1970. The primary drivers of this crisis include habitat destruction, overexploitation of resources, pollution, illegal wildlife trade, invasive species and climate change.

In response to these unprecedented losses, immediate and coordinated international action is required. Every country and territory must actively engage in biodiversity conservation to halt further biodiversity decline. Nations need to implement effective policies and conservation strategies tailored to local ecosystems, while leveraging global frameworks such as the Convention on Biological Diversity (CBD) and the United Nations Sustainable Development Goals (SDGs).

One of the significant barriers to effective conservation is the impact of
and potentially supply reductions elsewhere.<sup>18</sup> Despite this, the entry rate spiked to 42 percent. The separation rate also fell, as firms held on to previous

> 18 Total import values reported by the Census Bureau: https://www.census.gov/foreign-trade/balance/c0015.html. In the Panjiva data the volume of container shipments also declines in March to May 2020 relative to March to May 2019.

22
Table 1: Baseline Population and Expenditure Statistics, in US$ at PPP

|Country|Population|Number of<br>households|Total<br>expenditures|Per capita<br>expenditures|Household<br>expenditures|
|---|---|---|---|---|---|
|Djibouti|939,000|166,966|1,856,869,376|1,977|11,121|
|Iran, Islamic<br>Rep.|77,969,000|21,909 116|582,976,929,792|7,477|26,609|
|Libya|6,213,000|991,549|1, 318,968,832|1,983|12,424|
|Morocco|33,179,000|7,070,798|138,34, 810,176|4,170|19,565|
|Tunisia|11,060,000|2,548,655|43,800,788,992|3,960|17,186|
|Total|129,360,000|10,777,968|628,634,588,160|3,913|17,381|

_Source:_ World Bank estimations from Household Budget Surveys.

_Note:_ PPP = purchasing power parity. Data on household expenditure per capita can be very different from data on GDP per capita and the cross-country ranking made according to these two criteria can be quite different. This is mostly explained by the fact that total household expenditure represents different shares of GDP across countries.

# Baseline Prices and Subsidies

As a reference period for the analysis, we use the very early part of 2014 when oil prices and subsidies peaked at their highest levels. A major wave of subsidy reforms occurred in the MENA region in 2014 but this paper focuses on the extraordinary situation faced by MENA countries before
2%) of all basic education school‐age children residing within the country were not attending school by 2014‐2015. They estimate a decline of 1.5 in the average years of schooling for population aged 15 and above _,_ based on these non‐attendance rates, from 6.8 in 2010 to 5.3 years in 2015.<sup>32</sup> We provide alternative calculations to assess the impact from the interruption of schooling as well as migration and fatalities on the national average of years of schooling. To do this, we use forward extrapolations, with the following assumptions:<sup>33</sup>  for age groups, a�3: 25 �29 to a�10: 60 �64 , the educational attainment (average schooling years), s, of gender g (either men, m or women, f� in age group, a, at time, t, is the same as that of the age group five years younger at time, t�5, as we assume these groups have completed their education, i.e.,

> 31 Though this LFPR increase contrasts with the ILO estimate of a decline, data in World Bank (2017b) suggests a sharp increase in the unemployment rate from 8.6% in 2010 to 52.9% in 2015. ILO estimates, on the other hand, point to a more moderate increase in the unemployment rate to 14.9% in 2018 (14.7% in 2015).

> 32 The same contraction rate in enrollment for basic education is assumed for secondary and tertiary education.

> 33 The nomenclature draws on Barro and Lee (2013, 2015).

15
. We used a simple average for each country for the period 2014-2019.

> 20 See, for instance, Barro (1991), Levine and Renelt (1992), and Durlauf (2009) for an overview.

> 21 See, for instance, Glewwe (1991), Mensah et al. (2014), Christiaensen and Hill (2019), Corral et al. (2020) and the numerous World Bank country-specific poverty assessments.

> 22 The list of potential mediators is also influenced by the pragmatic reason of data availability. To be included as a potential mediating variable, we require data for all countries and time periods included in our sample of spells.

> 23 Data on intergenerational educational mobility are taken from Narayan et al. (2018).

10
table 1). In July 2004, CBRC announced that **100 foreign banks** operating in China were allowed to provide banking services in local currency in 13 cities. Foreign bank’s renminbi-denominated assets reached 84.4 billion yuan (US$10.2 billion) at the end of June, up 49 percent, year-on-year, indicates by CBRC’s data (China Business Weekly July 27, 2004). CBRC also loosened rules regarding foreign financial institutions’ capital requirements to “promote their healthy growth”. (China Daily August 05, 2004)

**Table 1:  Foreign Ownership in China’s Local Banks**

|Local Banks|Investing Foreign Banks|% of<br>Shares|
|---|---|---|
|Bank of Communications|HSBC (Shanghai)|19.9*|
|Shenzhen Development Bank|New Bridge Capital|17.89|
|Shanghai Pudong Development Bank|Citi Bank|4.6|
|China Minsheng Banking Corp.|IFC|1.6|
|Industrial Bank Co.(Fujian)|HSBC/ Government Investment Corp. of<br>Singapore/IFC|16 / 5 / 4|
|China Everbright Bank|China Everbright Group (Hong Kong)<br>/ADB|20.1 / 3|
|Bank of Shanghai|HSBC/IFC/Shanghai Commercial Bank|8 / 7 / 3|
|Nanjing City Commercial Bank|IFC|15|
|Fujian Asia Bank(a joint venture<br>bank)|HSBC/Ping An Insurance Group|50 / 50|
|Xi’An City Commercial Bank|IFC/ Sotia Bank|12.5 / 12.4|
|Dalian CityCommercial Bank|SHK Financial Group|10|

Source: Securities Market Weekly July 2004. *This deal was completed in August 2004.

18
2.5|128|106|
|Resource intensive|2.35 2.40|2.21<br>2.07|2.58|2.14|1.73|1.82|5.4|88|71|
|Medium- to high tech|0.09 0.08|0.09<br>0.10|0.09|0.11|0.15|0.16|13.3|102|182|

Source: Own calculations based on EU-15 trade data reported to the UN COMTRADE database.

As expected, the specialization profile in EU markets, as obtained through running the trade data through the above discussed “technology” filter, does not diverge overall from the export basket in terms of factor intensities. Its export basket embodies mainly resource and labor
# **APPENDIX C: VARIANCE OF MEAN INCOME GROWTH**

<!-- Start of picture text --> The standard deviation of mean income growth is presented in Figure C 1, Appendix C. This country-specific variability in ROCs is<br>generally higher than that faced by NROC economies (Figure C 1, Panel B, Appendix C). In terms of their median, LOGCs present<br>larger standard deviations of mean income growth than HOGCs and MOGCs, with MOGCs representing the economies with the smallest<br>median value (Figure C 1, Panel C, Appendix C). Furthermore, via a standard variance decomposition of the OLS estimates of Equation<br>(1), we identified three main features of the dispersion of the growth in the mean income (Figure C 2 in Appendix C). First, idiosyncratic<br>factors drive the variability in income growth faced by all countries and country typologies. 37 Second, global factors affecting income<br>growth appear to be less relevant in ROCs and LOGCs. Third, the covariance component between the growth of global income per<br>capita and global commodity prices is negligible, c ] ≈0, over the period 1980–2014 and across countries.<br>FIGURE C 1. VttARIABILITY OF tt MEAN INCOME GROWTH<br>vv[gg , pp<br>Panel A. All countries Panel B. Resource typology Panel C. Output growth typology<br>16 16 16<br>12 12 12<br>8 8<br>8<br>4 4<br>4<br>0 0<br>0 NROC ROC HOGC LOGC MOGC<br>Annual growth (percentage) Annual growth (percentage)<br>Annual growth (percentage)<br><!-- End of picture text --> _Source:_ Penn World Table 9.0, World Development Indicators, United Nations, and the author’s estimates
Exporter Dynamics Database described in Fernandes et al. (2016). These three countries share many similarities in their economic and trade structures and constitute an excellent laboratory for studying the impacts of PTAs as they were signatories to close to 40 new PTAs with a variety of trading partners that entered into force during the sample period.

Our empirical strategy isolates the change in firm export performance driven by the regula-

> 4Common standards within a PTA can be existing international standards (e.g., the Codex Alimentarius Committee standards for food safety) or new regional standards developed by the PTA members.

> 5Schmidt and Steingress (2019) argue that an expected benefit from harmonization is higher demand if harmonized rules are based on consumer preferences and on compatibility between products across countries.

2
# 3.5. Continent-wide estimation method

Once the financial costs (private and public) per user have been estimated for each of the six detailed country assessments, the mean cost per user is extracted for each population density decile. To scale the results across the African continent, the mean cost per user is multiplied by the population in each local region to gain an estimate of the required total investment, representing the cost for all network operators to achieve universal broadband. Such a method relies on the fact that population density is the main driver of the cost of infrastructure delivery (Oughton and Russell, 2020), because large upfront fixed capital costs are required to build infrastructure assets before a single user can be served (Hall et al., 2016b). Figure 5 illustrates the population density for all local regions extracted from the Database of Global Administrative Areas (GADM, 2019) using the WorldPop settlement layer (Tatem, 2017; WorldPop, 2019). The importance of strategically mapping spatial rural broadband information is well recognized (Hambly and Rajabiun, 2021). Further information on the decile segments can be found in Table S1 of the Supplementary Materials.

21
only. Also, talking about conflict events might be psychologically burdensome, which is why keeping the interview short is particularly important. Finally, people living in conflict-affected areas might be afraid to be seen reporting these events to enumerators who work for the Nigerian government; therefore, they might feel more comfortable reporting such sensitive events over the phone. Administering the conflict module in a separate telephone-based survey allowed us to get detailed information on the events, without placing a large burden on the households during a long household interview.<sup>4</sup>

> 3 Similar telephone surveys have been conducted in six countries in Sub-Saharan Africa under the World Bank project Listening to Africa (Croke et al. 2012).

> 4 Telephone surveys would also be recommended in situations with high migration, which might lead to systematic attrition. The migration rates in the LSMS panel were very low between 2010 and 2016, and in our telephone survey sample, all households had stayed in the same local government area since 2010.

5
|10.3|
|9|0.8|1.4|4.2|0.7|1.5|4.8|0.8|1.4|5.2|
|10|0.3|0.5|1.8|0.2|0.5|1.6|0.2|0.4|1.8|
|Total|**17.5**|**23.7**|**36.6**|**17.1**|**23.5**|**36.1**|**17.3**|**23.2**|**36.4**|

# _Welfare impact of improving targeting efficiency_

We have so far examined the PMT formula from the perspective of targeting efficiency. A related, and equally important question is: what kind of welfare improvements we can expect with the application of these formulas in transferring benefits to eligible beneficiaries? We present one case which could serve as a useful example, although there are multiple ways in which we could answer this question. Narayan and Yoshida (2005) discuss more scenarios and describe the basic methodology in detail.

Table 5 presents the welfare outcomes, measured by the Foster‐Greer‐Thorbecke (FGT) index under three scenarios. The first case is the baseline data collected in HIES 2016, in which household consumption includes Samurdhi benefits. The poverty headcounts at the national poverty line of Rs. 4,166 per month and the $3.20 per day for lower middle‐income countries in this baseline scenario are 4.08% and 9.50%, respectively. In the second scenario, Samurdhi benefits are subtracted from the consumption expenditures of all households. The third scenario modifies the second scenario by re‐introducing the Samurdhi benefits (Rs. 1,732 per capita) for households
## **5.7 Placebo tests**

Finally, we perform some placebo tests to ensure that the effect captured is essentially that of the constraints observed in the power sector. To do this, in Table (15), we have dummy variables (1/2 1/2), (1/3 2/3), (2/3 1/3), (1/4 3/4) and (3/4 1/4). When replacing each of these random dummies in our baseline model, we find no statistically significant effect on firm-level revenue-based total factor productivity.

Table 15: Placebo tests – The non-significant effects of random treatment variables on the firm’s revenuebased TFP

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Random treatment (1/2 1/2)|0.012<br>(0.021)|||||
|Random treatment (1/3 2/3)||-0.002<br>(0.021)||||
|Random treatment (2/3 1/3)|||-0.025<br>(0.022)|||
|Random treatment (1/4 3/4)||||-0.006<br>(0.017)||
|Random treatment (3/4 1/4)|||||0.030<br>(0.019)|
|Observations|31406|31404|31404|31405|31404|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variables is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-5)
many societies labor market expectations across genders are different, resulting in different emotional attitudes towards employment. How can we reconcile the objective status of labor deprivation with subjective feelings of labor deprivation across social groups?

This paper uses the concept of relative deprivation to better understand how objective conditions of labor deprivation can be transformed into subjective feelings of labor deprivation for different social groups. We focus on youth and gender using a unique household and youth survey administered in Morocco in 2010 and we propose a new measure of relative labor deprivation that takes into account both objective and subjective aspects of deprivation.

The concept of relative deprivation is not new. Both Adam Smith in the Wealth of Nations (1776) and Karl Marx in Wage, Labor and Capital (1847) delved extensively into the idea that people compare themselves with their peers and neighbors rather than with the whole society. In the last century, Stouffer et al. (1949) and Runciman (1966) contributed to formalize the concept of relative deprivation by introducing the notion of reference groups and by discussing the importance of the reference group in generating feelings of deprivation. Economics has approached the question of relative deprivation and reference groups in more recent times with the introduction of the first quantitative measure of relative deprivation developed by Yitzhaki in 1979, and today there are a range of measures of relative deprivation that can be readily used for analyses (Hey and Lambert, 1980, Bossert and D’Ambrosio, 2006, Chakravarty et al. 2005, Chakravarty, 2007). In particular, recent research has shown how 2
on the 2009 - 2018 period, since further corporate income tax reforms were introduced after 2018. We also drop firms that are not liable for income tax regimes; state-owned firms; and "Individual Enterprises" ( _Personnes Physiques_ ), which should not be liable for the Corporate Income tax. These restrictions reduce our sample to approximately 198,000 firms. Finally, we also drop what we define as "never active" firms – these are firms that are observed at some point throughout the period in the RNE dataset but never register any activity (i.e. positive revenue in tax data, employment in social security, exports or imports in customs).

The sample we obtain, which is the main one we use for the following analyses, is comprised of approximately 96,000 onshore firms and 22,660 offshore firms. In Table 1, – we provide descriptive statistics for this sample in column (1) for the entire panel, and in columns (2) - (6) for selected years. We also document the characteristics of the offshore and onshore sectors in 2013, the year prior to the reform. Approximately 20% of firms in our sample are registered in the offshore regime and 10% of total firms are foreign owned. In terms of sectors, one-third of firms are in manufacturing and one-third in wholesale or retail; the remaining firms are mostly in the services sector (25%).

This is a period of significant expansion in the number of firms in the registry (and our sample), with close to 6 - 7% of firms each year being "entrants", defined as firms we do not observe previously in the panel. The average age of firms is 10 years, but the median is 7 years.

Before providing descriptives for the outcomes of firms in each year, we note that often we observe firms in a given year in the RNE data but information on outcomes can be missing. For that reason, we first document the share of non-missing observations
ly different correlations. The model predicts either an empirical finding that higher withincountry correlation between morality and ability (controlling for the levels of both) has a positive effect on economic performance in a cross-country regression, or that over the relevant range the sector choices of high-ability agents are much less sensitive to morality than those of low-ability agents. We test the theory using the World Values Survey as a source of proxies for morality and borrowing the regression framework of Rodrik, Subramanian and Trebbi (2004). We show that higher within-country correlation between morality and ability, holding constant the levels of morality and ability, increases per capita income levels. This effect is both robust and of a substantial magnitude: using our preferred proxy and specification, a one-standard-deviation increase in the correlation raises the log of per-capita income by about one-fourth of a standard deviation, equal to approximately $3600 for the median-income country in our sample.

The remainder of the paper is organized as follows. Section II summarizes the relevant literature on morality (as we define it in this paper) and on the importance of how talent is allocated between productive and appropriative economic activities. Section III lays out our model and derives our theoretical results. Section IV contains our empirical results, including analysis of causality (that the morality-ability correlation causes high incomes and not the other way around), and of the robustness of the results to using alternative proxies for morality. Section V contains discussion and conclusions.

# **<u>II. Previous Literature</u>**

# _A. Morality_

For this paper to have relevance, it must be the case that at least some agents are willing to sacrifice income in order to do work that they regard as more moral. Casual empiricism suggests 4