rafmacalaba/data-use-mentions-extended huggingface dataset:
The production by energy types does not have an exact correspondence in the IO table. The link is thus created by mapping the energy types to the energy sector.

The IO contains four energy related product categories, as listed in the table below.

|**Sector**|**Sub-sector**|**Product**|
|---|---|---|
|**Manufacturing**|Mining|Coal and Lignite; and Peat|
|**Manufacturing**|Mining|Crude Petroleum and Natural Gas (Distribution through Main)|
|**Manufacturing**|Manufacturing|Electricity, Town Gas, Steam, and Hot Water and Natural Water|
|**Manufacturing**|Manufacturing|Coke Oven Products; Refined Petroleum Products; and Nuclear<br>Fuel|

The supply and use tables show that the two mining sectors sell their output mostly to the refining sector. We thus consider the bottom two sectors from the table as the energy sector for the purpose of this project. Total value added will thus be given by K K K K K , Which correspond to agriculture (AGR), services (SRV), non-energy manufacturing (MNF(Oth)), and FFCCSSDD VVGGDD,tt SSDDVVtt IIKKDD,tt EE,tt YY = VVAA= YY + YY + YY + YY Which correspond to agriculture (AGR), services (SRV), non-energy manufacturing (MNF(Oth)), and “energy” (ENE). The energy sector uses fossil fuels and renewables so that we obtain the following nested CES structure.

<!-- Start of picture text --> VA<br>Energy sector<br>AGR SRV MNF (Oth) ENE<br>Coal Oil Gas Ren.<br><!-- End of picture text --> 26
information on assets in the household. The source data are a series of dummies where 1 is assigned if the household has the asset (television, car, radio, etc). We construct z-scores of the data and project them onto a new basis using the eigenvectors that decompose the covariance of the data. Intuitively, we are using the information of asset ownership to rank households according to their ownership of goods, giving more weight to the information that explains the largest part of the variance in the asset dummies.

21
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<!-- Start of picture text --> 2000 12000<br>Data - interquartile range Data - interquartile range<br>1800 | YY) Simulation - interquartile interquartile range ') Simulation Simulation - interquartile interquartile range<br>| -———~ Simulation Simulation average ——~ Simulation average average<br>= 1600 +sse«Data average average 40000. +sss«Data average average<br>= _ | ee<br>S
insufficient wealth are likely to default on rent payments. This implies that landlords will tend to enter into fixed rental contracts only with tenants who are wealthy enough to pay the rent under all possible output realizations, implying that poorer tenants will be offered only a share contract (Shetty 1988). Finally, a dynamic setting opens up a number of additional perspectives besides the scope for using the repeated game context and the threat of eviction to reduce the efficiency losses of sharecropping. On the other hand, a rental contract that provides tenants with adequate incentives to maximize profits in any given time period may lead to overexploitation of the land if (dis)investment is considered, implying that a share contract with lower-powered incentives and possibly compensation may be more appropriate (Ray 2005).

A large literature has focused on testing the extent of inefficiency involved in sharecropping contracts, with often mixed results (Otsuka and Hayami 1988). Within-household data suggest that, in India, share tenancy is associated with an average productivity loss of 16% (Shaban 1987). Part of the losses might have been policy-induced as fixed rental contracts were outlawed in the study area. Recent studies using household fixed effects suggest that, in light the constraints faced agents‟ choice of contractual arrangements is rational 7
**Figure 7:  Evolution of export volumes and share for different sectors**

<!-- Start of picture text --> 160 80%<br>120 60%<br>80 40%<br>40 20%<br>0 0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Agriculture ($) Mining ($) Manufacturing ($)<br>Agriculture (%) Mining (%) Manufacturing (%)<br>Million USD ($)<br>Fraction of total (%)<br><!-- End of picture text --> Note: The solid lines show absolute export volumes (in USD) on the left-scale; the dashed lines show the relative share of the three sectors in the total (in percentage) on the right-scale. We do not report the absolute export volumes in 2016. These are notably lower for all three sectors, but we are uncertain to what extent this is due to incomplete reporting or an accurate reflection of the export evolution.

But the greater relative importance of manufacturing is a welcome evolution for several reasons. The upside potential is less likely to be capacity constrained than in the other two sectors. Compared to mining in particular, the greater labor intensity of manufacturing is good for the employment potential. Prices tend to be less volatile than in agriculture or mining and the aggregate export volumes shown in Figure 7 are least volatile for manufacturing. The more broadly based export market participation that we documented for manufacturing in Figures 5 and 6 can be one factor contributing for the greater stability. Exports in the other two sectors are more sensitive to idiosyncratic firm-specific successes or failures.

Using the sectoral data from EORA we can construct the growth rate in the share of output that is exported for the three sectors. For agriculture and mining the trend is not positive. The share expanded from 1995 to 2005, but contracted from 2005 to 2015. For manufacturing there is an increase
12 percent of the US population.

The findings suggest that an improvement in the gender wage ratio among women in urban settings is mainly associated with a reduction in homicides and that impacts are primarily focused among younger women and in municipalities with a lower human development index. The impacts of changes in the gender wage ratio on less severe forms of violence appear to depend on the context. In municipalities in which women’s police stations have been established, a decrease in the gender wage ratio is associated with a reduction in reports of violence. In municipalities without women’s police stations, the analysis found the opposite effect, a decrease in the gender wage ratio is associated with a rise in reports of violence. Taken together, the results suggest that, while women’s economic empowerment alone may contribute to a reduction in rates of the most severe violence, its effects on less severe forms of violence largely depend on the context. If not paired with civil and legal infrastructure that protects women, represented in the case of Brazil by women’s police stations, women’s economic empowerment appears to trigger a backlash among men. However, in conjunction with protective mechanisms targeted at women, women’s empowerment tends to lower the incidence of less severe forms of violence as well. Overall, the study confirms that policies aimed at narrowing the malefemale wage gap reduce the likelihood of violence in Brazil, an upper-middle-income developing economy. However, the findings also highlight the importance of additional policies specifically aimed at the protection of women against violence to maximize the positive impact of advances in gender equality in the labor market on the reduction of violence.

# **Appendix**

The analysis uses the RAIS to generate the gender wage ratio. This matched employer-employee data set contains data on all the labor market contracts in the formal sector in Brazil. However, in Brazil, 20 percent of laborers work in the informal sector.<sup>13</sup> In this section, the nationally representative household survey, PNAD,
Table 8: Out-of-sample dynamic forecast performance (RMSE, percent)

||||All cou|ntries|||
|---|---|---|---|---|---|---|
||Trade|weight|matrix|Distan|ce weight|matrix|
|Forecast horizon|1 year|2 years|3 years|1 year|2 years|3 years|
|CSD specifcation|||||||
|None|2.870|4.605|3.733|2.870|4.605|3.733|
|Factor only|2.133|3.965|3.337|2.133|3.965|3.337|
|Spatial only|2.262|4.077|3.255|2.333|4.203|3.376|
|Factor and spatial|2.094|3.951|3.316|2.146|3.981|3.357|
||||Advanced|countrie|s||
||Trade|weight|matrix|Distan|ce weight|matrix|
|None|2.079|5.143|1.955|2.079|5.143|1.955|
|Factor only|1.541|4.562|1.016|1.541|4.562|1.016|
|Spatial only|1.930|5.045|1.671|1.926|5.050|1.690|
|Factor and spatial|1.521|4.498|0.957|1.491|4.475|0.999|

Notes: The table shows the RMSE of dynamic forecasts over 2014-2016 obtained with model estimates using data for 1970-2013 under alternative specifications of cross-sectional dependence. Specifications including a common factor use an AR
# **INTRODUCTION**

Questions of how to effectively reform and strengthen tax systems have moved to the center of development debates. On one hand, there is an opportunity to build on the successes of tax reform efforts in recent decades. Across much of the developing world, countries have achieved significant administrative and policy modernization, and meaningful improvements in revenue mobilization. On the other hand, successes have been uneven, and major challenges remain. Tax revenue in many countries remains well below the levels needed to finance the achievement of the sustainable development goals, with 15 percent of GDP often cited as a rough annual target (Figure 1).<sup>1</sup> Meanwhile, revenue collection is frequently characterized by significant unfairness and inequity, with particularly weak compliance and enforcement among the rich, and often significant – though overlooked – formal and informal burdens on lower income groups.<sup>2</sup>

**Figure 1: Total Non-Resource Tax Collection by Region, 1990-2016 (% of GDP)**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015<br>EAP ECA LAC MENA NA SA SSA<br><!-- End of picture text --> _Source:_ International Centre for Tax and Development/UNU-WIDER Government Revenue Dataset Observers have questioned whether recent improvements in revenue collection have contributed to the construction of broader “fiscal contracts”. Revenue collection is not an end in itself. It only becomes socially desirable if it is translated into efficient and effective spending by governments. There is now extensive research documenting the ways in which the expansion of taxation may spur greater accountability and contribute to state building. Yet the research makes clear that while

> 1 Gaspar, V., Jaramillo, L and Wingender, P. (2016) Tax capacity and growth: is there a
in the Appendix shows how this weighted index is constructed. The SPEI-6 and SPEI-24 index time scales correspond at the beginning of the household survey period. The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2 or ADM 1, we use the aggregate equation i m ); a )} where a is for absolute value and is max or min depending on the result of the conditional function. ii mm mm mmiimm SSIISSII = SSIISSII mmaamm{a aa(SSIISSII aa(SSIISSII aa mm The income equation (equation 4) of the model includes the community variables Z and C from the SEDLAC and the CCKP data. On the other hand, the remaining community variables N are included in the expected variance equations (equations 5, 6 and 7). We assume that the community variables from household survey (Z) and climate change (C) can offer valuable insights into the expected average income. These variables are not prone to quick variations in variance and some of them change slowly, however they can still have a significant impact on income distribution shifting the distribution and changing the expected mean. The remaining community variables are considered more volatile and unpredictable and are better suited to explain the expected variance of income.

# **_Variables used in the assessment_**

We use the per capita household income adjusted by countries’ CPI and PPP 2017 as the main wellbeing outcome of interest. We also adopt an international poverty line adjusted by PPP 2017 of 6.85 USD daily. We select as household-specific variables for LASSO the following: salaried household head, labor informal household head, the household dependency ratio (number of members of the household divided by the number of income earners in the household); if household head’s main principal income is from agricultural activities, dwelling's materials of low quality, dwelling with toilet facilities linked to sewer, access to electricity, access to safe water, overcrowding (more than three members per bedroom), the
- ➢ FDI in sector _u_ could generate indirect effects on sector _j_ through forward linkages. Javorcik (2004) and Newman et al., (2015) show that a foreign firm in the upstream sector _u_ can also transfer improved technologies to the domestic affiliate in the downstream sector _j_ . In our conceptual framework, there is no reason to believe that country border matters. Therefore, FDI in the sector _u_ in a foreign country _f_ might also have effects on sector _j_ in the country _i_ .

- ➢ FDI spillovers could also take place through backward linkages. FDI in sector _d_ could affect sector _j_ . Javorcik (2004) provides an anecdotal case of the Czech producer of aluminum alloy castings and its multinational customer. In this case, the staff from the multinational firm would visit the domestic suppliers two days in each month, and the domestic suppliers applied these improvements to its other production lines. FDI in the foreign downstream sector _d_ in country _f_ might also generate spillovers to sector _j_ in country _i_ .

In the next section we want to test the strength and causal relations of the arrows depicted in Figure 5 in the last decade.

# 4 Data and Metrics

## 4.1 Data

To implement the empirical analysis, we link data on FDI flows across countries available since 2003 with data on input and output linkages. The main sources are fDi Markets data monitored by the Financial Times and the MRIOT compiled by the Asian Development Bank.

- ➢ The fDi Markets is the most comprehensive online database of cross-border greenfield investments; the database covers all countries and sectors. For a specific FDI project, it documents the source and destination country, the destination sector, the number of jobs, the amount of investment, and the industry classification.

- ➢ For the measure of the GVC participation of a sector in a country, we need a data set on international input-output tables (IOTs). The Asian Development Bank’s Multi-regional input-output tables (MRIOT) version published in 2019 covers 61 individual economies-and the rest of
recommend that rather than focusing on inputs, the focus should shift to other policies, including giving right incentives for teachers, and increasing school competition.

The importance of incentives is confirmed by a study of Woessman (2003) who estimated an education production function based on student-level data for 260 000 students from 39 developed and developing countries<sup>12</sup> . He finds that school autonomy (teacher incentives) and centrally controlled examinations (student incentives) do positively and significantly influence educational performance. He does not consider the school size variable, however, while _higher class size has a positive effect on education outcomes._ There is one cross country study that focuses on transition countries. Ammermuller et al (2003) estimate an education production function for seven Eastern European countries (excluding Russia) using the TiMMS 2003 wave. Their study finds that _class size has a positive influence on test scores, only if one does not control for selection effects_ . Once selection effects are controlled for the coefficient of class size becomes insignificant. Other included school characteristics, such as shortage of materials and measures of teacher and school autonomy were also found to be insignificant in most cases.

# _b. Single-country studies_<sup>_13_</sup>

Single transition countries studies so far focus on the Central European countries with, as far as we could find, no evidence being available about countries which belong to the Commonwealth of Independent States (CIS).

Three studies use Polish data to study the determinants of educational performance. Herczynski and Herbst (2005) find _a small positive effect of class size_ . They also find that increased school choice improves achievement but only up to a certain threshold, after which the opposite effect takes place. Bukowska and Siwińska-Gorzelak (2011) find _a significantly positive effect of school size_ in almost all their specifications. They also find a positive influence of school competition (and accordingly a negative effect of the Herfindahl index) on students‟ test scores.

> 12 This country included the same eight transition countries
, or of better quality candidates that improve the platforms of political competition. Humphreys and Weinstein (2011), for example, find no impact on politician behavior in Uganda. Bobnis and Fuertes (2009) show how politicians can anticipate voter information, and simply change the timing of rent-seeking.

Furthermore, in contrast to hot-button issues such as corruption, broad social services such as health and education may particularly suffer from clientelist political competition, and quietly,<sup>33</sup> because the poor may have lower demand for these services, in contrast to private targeted transfers. There is a large literature examining how and why poor households would be likely to under-invest in and under-demand health and education (Banerjee and Duflo, 2011), which can spill-over to their expectations from governments or politicians.<sup>34</sup> A growing body of research shows that when it comes to services in health and education people can use information to change their private behaviors but not their demands and expectations from

> 31 Schaffer (2007) also discusses the role of law enforcement policies to crack-down upon and punish politicians who engage in vote buying, providing examples of successful cases from Taiwan, China.

> 32 Although, Collier and Vicente (2008) find that an NGO in Nigeria was able to design and implement a campaign to impact the use of violence as an electoral strategy.

> 33 The World Bank’s Africa Development Indicators 2010 described the failures in pro-poor public service delivery as “quiet corruption”, not making news headlines as bribery or theft scandals do.

> 34 In the Philippines survey studied here, for example, respondents appear constrained in evaluating municipal performance in delivering health services: their ranking of health services is not correlated with more objective indicators of access to services. There are several instances (about 19 percent of respondents) where a household ranks municipal performance in delivering health services very high, while responding on a separate question about their perceptions of child health outcomes that more than half the children in
# **1. INTRODUCTION**

Firms invest in a range of knowledge-based or intangible resources such as computerized data and software, patents and trademarks, employee and management skills, or organizational improvements. Although they do not directly increase the level of physical assets, such knowledge-intensive expenditures resemble investment in traditional fixed capital such as machinery, equipment and buildings in that the benefits from them are not exhausted in a single year. Hence, it has been argued that such expenditures build ̳intangible assets‘ or ̳knowledge-based capital‘. Increases in the stock of such knowledge-based capital are now increasingly recognized as an important driver of innovation and economic growth.

Intangible assets cannot be observed or quantified in the traditional sense. Some recent studies have proposed ways to deduce intangibles from observable tangibles using economic theory.<sup>2</sup> For example, one approach uses the value of securities to infer the quantity of intangible capital held by U.S. corporations (Hall 2000).<sup>3</sup> But the method which has become the most accepted in recent years estimates intangibles directly from observed expenditures. Building on previous studies that examined the undervaluation of US business investment in the late 1990s, Corrado, Hulten and Sichel (2005, 2009; henceforth CHS) proposed a comprehensive categorization of intangible investments, and quantified them by piecing together the corresponding expenditure data for the US.<sup>4</sup> The underlying principle is that any business outlay intended to increase future rather than current consumption should be treated as capital investment.

Spending on most intangibles is treated as intermediate expenditures in conventional national income accounting, which assumes that all their benefits are captured in the current output of tangible goods.<sup>5</sup> If one accepts the CHS framework, this causes an underestimation of total levels of investment and output.

> 2 See OECD (2011) for a summary of recent work on measuring intangibles. Fereira and Hamilton (2010) compute  ̳intangible capital‘ as
Note: Z-scores are calculated based on the entire Enterprise Survey sample. Corporate ESG _weighted_ : 40% for E, 25% for S, 35% for G. Source: Authors’ calculations based on Enterprise Survey data.

MENA countries’ social component score, on the other hand, is the lowest among all regions. The gender subcomponent – which, together with education and training of the workforce, is determining the S component – is particularly weak. Female participation in the workforce in MENA countries is low: the share of female workers in MENA (considering the firms that are part of the Enterprise Survey) is 25%, significantly lower than in the other areas (apart from Turkey). The female representation in management is poor: only 5% of MENA firms have females in management, while this ratio is more than 20% in Russia, Central and Eastern Europe and Central Asia. At the country level, Morocco and Tunisia have more female participation in the workforce (higher than 30% of total workforce in the Enterprise Survey sample) and Tunisia has a better performance in terms of presence of female management as well (10% of the firms). In this respect, the West Bank and Gaza and Jordan have the lowest presence of females in management (1% and 3% of firms respectively), with Lebanon, Morocco and Egypt around 5%6%.

**Figure 10 – Female participation** (percentage on total)

<!-- Start of picture text --> 50%<br>Firms with female in<br>management<br>40% Share of female<br>workers<br>30%<br>20%<br>10%<br>0%<br>Russia CEE CA EN WB MENA Turkey<br><!-- End of picture text --> Source: Authors’ calculations based on Enterprise Survey data.

As described earlier, ESG practices differ by sector. Since the ES