rafmacalaba/datause-encoder-data huggingface dataset:
often need concepts clearly defined (even if, as commonly seen, they may not act on the relevant information available). Consider these names: cultural industries, creative industries, leisure industries, entertainment industries, content industries, copyright protected industries, cultural economy, and creative economy. The names may differ slightly here and there, but they all have a ‘common area’ glued by creative work. The Inter-American Development Bank therefore proposed “a practical definition [called] the ‘Orange Economy,’ starting from the common area.”<sup>26</sup> Whether this definition will gain traction remains to be seen.

_Culture and Commerce_ The debate of art and commerce is often delicate. There are some who maintain that culture is culture. It is priceless and that it should not be commodified. As Kaushik Basu put it, “there are purists who shirk at the mention of art and commerce in one breath.” Moreover, “The recent headlines about how a group of international investors and speculators allegedly exploited several Chinese artists no doubt make us wary of the brew of art and markets.”<sup>27</sup> Nonetheless, the disinclination to consider sound strategies for cultural development because of the ‘purist’ ideology potentially hurts the promotion of comprehensive trade policy in creative fields.

# _The Intellectual Property Challenge_

## Figure 4

"Intellectual property (IP) refers to creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce.

IP is protected in law by, for example, patents, copyright and trademarks, which enable people to earn recognition or financial benefit from what they invent or create. By striking the right balance between the interests of innovators and the wider public interest, the IP system aims to foster an environment in which creativity and innovation can flourish.”<sup>28</sup> Source: WIPO In trade, intellectual property is one of the obvious measures to harvest earning from creative work. This is through copyright, patents, trademarks, and geographical indicators, for example. But this tool, which forms the basis of the WTO’s TRIPS framework, is complex, to say the least. It is often misunderstood and difficult to manage.

In areas like education and innovation, moreover, intellectual property can be a problem. It can inhibit the sharing of knowledge — this is so even if “the exclusive rights given

> 26 Felipe Buitrago and Iván Duque, _The Orange Economy: An Infinite Opportunity_ (Washington, DC: Inter-American Development Bank, 2013), 33-49, accessed April 7, 2015, <u>http://publications.iadb.org/bitstream/handle/11319/3659/BID_The_Orange_Economy%20Final.pdf?sequence=7.</u>

> 27 Kaushik Basu, _An Economist's Miscellany_ (New Delhi: Oxford University Press, 2011), 75. This issue is also discussed here: Patrick Kabanda. 2014. _The Creative Wealth of Nations._

> 28 World Intellectual Property Organization (WIPO) “What Is Intellectual Property?” WIPO, accessed April 11, 2015, <u>http://www.wipo.int/about-ip/en/.</u>

9
Policy Research Working Paper 10820

# **Abstract**

This paper studies the performance differentials between privately-owned enterprises and businesses of the state. Businesses of the State (BOS) are firms with 10 percent or more direct or indirect state participation. By analyzing firm-level data across 16 European countries between 2011 and 2020, the paper finds evidence that state ownership matters for operational and financial performance and sheds light on how and when it matters. The analysis disentangles the multiple forms of state participation and its effects on firms’ performance by exploring the heterogeneity across sector type, levels of state participation, and degree of separation (direct versus indirect shareholding). It also analyzes the early response of businesses of the state to the COVID-19 shock. The results suggest that businesses of the state underperform in terms of labor productivity, profitability, and return on investments, although the effects are heterogeneous depending on the level of state participation, degree of separation, and type of sector. Businesses of the state appear to be more financially leveraged vis-à-vis their private counterparts, suggesting potential soft budget constraints. Wider differentials in profitability and return on investments are evidenced when the state operates in fully competitive sectors that are viable for private participation, underscoring the opportunity costs of state ownership in those sectors. Furthermore, the findings show that mixed ownership with the private sector can drive better results when compared to fully owned businesses of the state. Similarly, a higher degree of separation from the state seems to improve performance, highlighting the role of corporate governance and ownership reforms to foster independence. Finally, businesses of the state demonstrated greater resilience in preserving jobs in the short term during the COVID-19 pandemic in 2020.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at dsancheznavarro@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Benefit Incidence Analysis: Are Government Health Expenditures More Pro‐rich Than We Think?

by

## Adam Wagstaff<sup>a</sup> ,

a Development Research Group, The World Bank, Washington DC, USA

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473‐0566. Fax (202)‐522 1153. Email: <u>awagstaff@worldbank.org.</u>

**Keywords:** benefit incidence analysis; government health spending; propoor health spending.

**Acknowledgements:** This paper grew out of a discussion with Aparnaa Somanathan, who also shared with me Vietnam’s latest NHA data. I am grateful to Marcel Bilger and Davidson Gwatkin for very helpful comments on earlier versions of the paper. The findings, interpretations and conclusions expressed and any errors are entirely mine; the views expressed do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.
(positively) correlated. Much like store-size, most store characteristics reported in the survey are only weakly correlated with non-workers (Table 3) which suggests that the omitted variable bias problem here is unlikely to be severe. Our empirical results confirm this in that the estimated coefficient of non-workers does not change much in magnitude due to controls for various store characteristics.

We address the potential problems discussed above by directly controlling for a number of city and store characteristics and by contrasting the effect on competition of non-workers and (1991 values of) the number of children per household averaged at the city level ( _Children_ ). Formally, _Children_ equals total number of individuals below 7 years of age in the city divided by total number of households in the city. Data source for the variable is Census of India (1991).

The motivation for comparing the effects of non-workers and children on competition has been discussed above. Our claim there was that non-workers and children are likely to be correlated with income and overall development of cities in the same direction. Table 3 reports on various correlations to support this claim. We note that the correlation between non-workers and children is high (.488) but not too high to preclude the estimation of their independent effects on competition. Further, we show that our result for the effect of non-workers on competition holds with and without controlling for children.

Other than _Children_ , controls in our main specification include a measure of household income which we proxy by the mean per capita expenditure of the district population ( _Expenditure_ ) and two proxy measures of overall development of the cities which are _Population_ and _Metro_ . Data on GDP or income levels are not available at the 9
are widely used. This section compares a few indicators of tax collection, which may shed a first light at the effectiveness of tax administrations in the various countries. It does not aim at fully asssessing the effectiveness of tax administration in the NM-EU countries as this would go beyond the scope of this paper.

91. Giving the tax administration the responsibility to collect and audit not only taxes but also social insurance contributions can lead to significant synergy effects and help reduce under-declared work. This can reduce administrative costs for both taxpayers and the administration and reduce evasion and fraud through better crosschecking and auditing. Countries with integrated tax and social security contribution collection in the region are Estonia, Latvia, Croatia, Hungary, Slovenia, Bulgaria, Romania, and Croatia. In Slovakia, the Czech Republic, Poland, and Lithuania social insurance contributions are still collected separately by social security institutions. As mentioned above, Slovakia and the Czech Republic are planning to introduce integrated collection systems.<sup>11</sup> The integration of revenue collection in these two countries is suported by the World Bank as was also the case in the other countries. For example, the Bulgaria Revenue Administration Reform project, supported by the World Bank, started in 2003 and is now fully implemented. The results of this reform appear to be very promising and compliance has increased (World Bank, Bulgaria, February 2009).

92. The effectiveness of collecting social insurance contributions varies considerably in the region. Table 12 compares the implicit ―productivity‖ of the social insurance contributions, calculated as the ratio between the revenue yield (as a percent of GDP) and the combined contribution rate (in percent). This indicator is, however only a very crude proxy for the effectiveness of collecting social contributions; it is also affected by the design of social security contributions, such as income ceilings and – most importantly – by the size of undeclared work. In Estonia, one percentage point of contribution rate yields 0.38 percent of GDP as revenue while in Romania it yields only 0.2 percent of GDP. Estonia receives higher revenue than Romania although Romania ́s contribution rate is 19 percentage points higher and it receives similar revenue as Slovakia, which has a more than 15 percentage point higher contribution rate.

> 11 Examples of integrated collection systems in other counties are United States, Canada, United Kingdom, Norway, Sweden, Italy, Ireland, Australia and New Zealand. By contrast in Germany, France and Belgium, social security institutions collect social contributions.

38
OECD 2022). For ease of illustration, the sample excludes small island nations – which cluster primarily in the low population-to-staff, low tax/GDP quadrant, and where the personnel to tax capacity relationship may fundamentally differ due to reduced scale and their prominence as tax havens – but the observed relationship is robust to including them.

**Figure 3: Population per Tax Staff and Tax-to-GDP**

Data Sources: Both measures are drawn from the International Survey on Revenue Administration (CIAT, IMF, IOTA, OECD 2022) using values for 2018.

Notes: This figure displays the relationship between tax revenues as a share of GDP and population per tax staff. Classifications of country income level are drawn from World Bank Development Indicators (YEAR). Low-income countries are represented by green triangles, lower-middle by blue diamonds, upper-middle by gray squares, and highincome by black circles. The line of best fit is displayed in blue, is estimated using all observations, and has slope - 0.002 (robust standard error 0.0002) which translates to an average decrease in the tax-to-GDP ratio of 2 for every additional 1,000 tax personnel.

The ratio of population-to-tax-staff is an order of magnitude smaller in high-income countries. For example, the United Kingdom reported 68,722 full-time-equivalent staff in 2018, or 1,133 citizens per staff, and Sweden 10,486 in total tax staff, or 1,064 citizens per staff. By contrast, middleincome countries like Pakistan (10,176 population per staff) and Ghana (10,006 per staff) – and low-income nations – e.g., DRC (12,811 population per staff) and Togo (12,582 per staff) – possess almost systematically higher ratios. Specialization of tax staff varies with the nature of the economy and the tax system. According to the CIAT, IMF, IOTA, OECD (2022) database, while the shares of staff assigned to registration, service, and payment activities (at roughly 30 percent) 11
**Table 4** : **_Stock & Flow Deficit_ – AHI and the Number of Inadequate Housing Units**

||||**Stock Deficit**|**– 2019**|**Flow Deficit -**<br>**2030**|
|---|---|---|---|---|---|
|**Country**|**AHI–**<br>**National**<br>**Average**|**Households**<br>**(in million)**|**Inadequate**<br>**Housing**<br>**Units (in**<br>**million)**|**Inadequate**<br>**Housing**<br>**Units per**<br>**capita**|**Inadequate**<br>**Housing Units**<br>**projected to**<br>**2030 (in**<br>**million)**|
|Albania*|0.90|0.78|0.08|0.03|0.07|
|Angola|0.63|6.30|2.21|0.07|2.88|
|Argentina*|0.97|13.62|0.40|0.01|0.44|
|Bangladesh|0.62|40.37|14.79|0.09|15.89|
|Belarus|0.91|3.87|0.31|0.03|0.28|
|Benin|0.49|2.80|1.41|0.12|1.83|
|Botswana|0.66|1.12|0.38|0.16|0.43|
|Brazil|0.95|72.36|3.43|0.02|3.56|
|Burkina Faso|0.45|2.83|1.46|0.07|1.91|
|Burundi|0.50|2.40|1.20|0.10|1.63|
|Cameroon|0.62|5.94|1.97|0.08|2.45|
|Chad|0.31|2.85|1.94|0.12|2.63|
|Colombia|0.87|15.14|1.83|0.04|1.87|
|Congo,Dem. Rep.|0.38|17.23|10.46|0.12|14.09|
|Congo,Rep.|0.58|1.26|0.53|0.10|0.67|
|Costa Rica|0.97|1.58|0.05|0.01|0.05|
|Côte d'Ivoire|0.63|7.27|2.85|0.11|3.60|
|Djibouti|0.54|0.19|0.09|0.10|0.10|
|Dominican Republic|0.92|3.45|0.29|0.03|0.30|
|Egypt,Arab Rep.|0.95|24.09|1.19|0.01|1.42|
|Ethiopia|0.42|24.47|14.07|0.13|18.14|
|Gabon|0.77|0.56|0.12|0.05|0.14|
|Gambia,The|0.62|0.35|0.13|0.05|0.17|
|Ghana|0.70|8.06|2.32|0.08|2.80|
|Guinea|0.46|1.98|1.05|0.08|1.36|
|Guinea-Bissau|0.37|0.23|0.15|0.08|0.19|
|Haiti|0.49|2.35|1.16|0.10|1.27|
|India|0.69|291.60|89.88|0.07|95.01|
|Indonesia|0.82|73.38|12.62|0.05|13.26|
|Iraq|0.84|5.86|0.94|0.02|1.19|
|Kazakhstan|0.98|5.42|0.11|0.01|0.13|
|Kenya|0.55|12.85|6.03|0.11|7.58|
|Lao PDR|0.62|1.39|0.51|0.07|0.56|
|Madagascar|0.34|6.16|3.77|0.14|4.91|
|Malawi|0.52|4.34|2.09|0.11|2.76|
|Mali|0.44|3.31|1.66|0.08|2.16|
|Mexico|0.90|35.57|3.35|0.03|3.54|
# **Annex IV.  Are Gross Revenues from Rice a Good Proxy for Rural Income?**

This policy note assumes that farmers with smaller plots or lower gross revenues from rice are worse off. This is because there are limited alternative activities (off-farm or beyond rice) that would compensate for small land size or low rice revenues. Although this is clearly an over-simplification, the evidence suggests that the assumption holds when looking at asset variables (e.g. quality of housing) in the survey and farmers with smaller plots of land or lower gross income from rice are worse off.

In the absence of an income variable from both surveys, the approach to determine if gross revenues from rice are a good proxy for wealth is two-fold: (i) use asset variables in the 2003 and 2007 surveys (the building materials of the farmers’ houses, the quality of the walls and flooring), and (ii) draw a subsample in 2003 that is limited to those respondents where rice accounts for a larger share of their income (50 percent or more) and look at assets in this sub-sample only. In both cases, there is evidence showing that farmers with small paddy fields and lower gross revenues are less well-off.<sup>20</sup> The findings from both 2003 and 2007 surveys suggest gross revenue from rice is a good proxy for rural income. Farmers in the poorer quintiles 1 and 2 report having houses of inferior quality materials, with soil floors and bamboo walls, as opposed to the higher quintiles 4 and 5, having non-soil floors and wooden walls. In both 2003 and 2007 surveys, half of those farmers with soil-floor houses belong to quintiles 1 and 2 (respectively, 27 percent and 24 percent in 2003 and 2007, see Tables IV.1-6). Likewise, 33 percent and 37 percent of the farmers in quintile 1 had bamboo walled housing, while 48 percent and 45 percent of the farmers having wood-walled houses belong to higher revenue quintiles 4 and 5.

## **Gross Revenue Quintiles**

## **Table IV.1.  Type of floor, 2003 Table IV.2.  Type of wall, 2003**

|Quintile|Non soil|Soil|Quintile|Brick(batako)|Wood|Bamboo|
|---|---|---|---|---|---|---|
|1|18%|27%|1|21%|16%|33%|
|2|19%|24%|2|21%|17%|26%|
|3|20%|19%|3|21%|20%|19%|
|4|21%|17%|4|20%|22%|14%|
|5|22%|13%|5|18%|26%|9%|
|Total|100%|100%|Total|100%|100%|100%|
|Total|36,226|9,886|Total|21,928|17,896|5,392|

**Table IV.3.  Type of floor, 2007 Table IV.4.  Type of wall, 2007**

|Quintile|Non soil|Soil|Quintile|Brick|Wood|Bamboo|
|---|---|---|---|---|---|---|
|1|19%|23%|1|18%|15%|37%|
|2|19%|23%|2|20%|19%|22%|
|3|20%|18%|3|20%|20%|18%|
|4|21%|18%|4|21%|21%|14%|
|5|21%|18%|5|21%|24%|10%|
|Total|100%|100%|Total|100%|100%|100%|
|Total|9,224|2,052|Total|5,746|3,686|1,663|

> 20 The assessment of farmers that sow rice more intensively can only be carried out in the 2003 survey that includes a variable determining the size of a farmer’s income derived from rice.

**30**
Since the mechanism to target and distribute food under the VL program is decentralized, it is useful to ascertain the degree to which this overall targeting reflects budget allocations to various districts and the degree to which it reflects within district prioritization. Nonparametric regressions (LOWESS) based on a census of first grade students in 1999 and VL expenditure data indicate that VL expenditure levels were targeted toward districts with higher levels of child malnutrition (Figure 1). Increases in VL expenditures between 1997 and 1999 are also slightly targeted to districts with higher levels of stunting (Figure 2).

To take this further, we decompose the targeting coefficients based on both poverty and nutrition using the method in Galasso and Ravallion (forthcoming). We find that interdistrict targeting dominates intradistrict targeting (left columns of Table 8). In other words, the degree of overall targeting (coverage of individuals less leakage) attributable to the central government's allocation to districts is greater than that attributable to allocations made within the districts by the municipalities and the mother's committees _(Club de Madres)._ The interdistrict contribution to overall targeting based on poverty status increased from 53 percent in 1994, to over 70 percent in 2000 when considering propoor targeting. Similarly, some three-quarters of targeting towards malnourished children can be attributed to targeting of districts as opposed to malnourished children within districts.

We note, however, that the decompositions that appear in the first set of columns in Table 8 are defined over population shares (i.e., share of poor in the program versus share of nonpoor in the program), not values. When the targeting coefficient defined over transfer values is decomposed (right-hand columns of Table 8), we find the opposite - that intradistrict targeting dominates interdistrict targeting. In 1997, the only year for which we are able to evaluate this form of targeting, over two-thirds of targeting took place within the districts, and less than a third can be attributed to interdistrict allocations. This follows from poor households within districts receiving larger transfers in value terms (consistent with the last columns in Tables 2 and 3, and to the last set of columns in Table 6) relative to the shares of the poor within districts participating in the VL program.

_16_
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