rafmacalaba/datause-encoder-data huggingface dataset:
affecting the frequency of innovations across countries. That is, countries with larger populations are expected to have higher counts of innovations than smaller countries. The following paragraphs provide further details about the data sources and control variables for each of the three search processes.

# **_On-the-Frontier Innovation_**

Our indicator of on-the-frontier innovation performance is patent grants by the U.S. Patent and Trademark Office (USPTO) collected by Lederman and Saenz (2005). The data are available from 1963 to 2001; however plant and design patents are only available after 1978. The period 1963 to 1981 is used to identify historical patent counts, and the 20-year period 1982-2001 is divided into four 5-year panels. The sum of patents within each panel is the indicator of performance in on-the-frontier innovation for that period. The Gini index is calculated as described above across the 43 industry categories, plus design and plant categories, available from the USPTO.

Because the incentive to patent an invention in the United States will largely depend on the importance of that export market to the particular country, we control for per-capita exports to the United States during the period. In addition, we control for investment in research and development (R&D) per capita which has been shown to be a highly significant determinant of patenting activity by firms as well as across countries (Hausmann et al. 1984; Lederman and Maloney 2003; Blundell et al 2000; Bosch et al. 2005, among others), and population to capture the effects of scale. Distance to the global technological frontier is captured by controlling for GDP per capita in quadratic form, and period indicators are added to capture temporal trends. Finally, we control for the historical level of patenting activity to capture country fixed-effects in patenting, and the 9
**Annex 1. Variables used in regressions**

|**Variable**|**Explanation**|**Time**<br>**Frame**|**Source**|
|---|---|---|---|
|growth_gdppc|Growth in GDP per capita<br>(constant LCU)|1964-2013|WDI|
|growth_cons.pc.|Growth in per capita final consumption expenditure<br>(constant LCU)|1964-2013|WDI|
|growth_manuf.|Growth in value added in manufacturing<br>(constant LCU)|1964-2013|WDI|
|std_gdppc|Standard deviation of the growth in per capita GDP<br>(constant LCU)|1964-2013|WDI|
|std_cons.pc|Standard deviation of the growth in per capita final<br>consumption expenditure (constant LCU)|1964-2013|WDI|
|privatecredit|Private credit by deposit money banks as a share of<br>GDP (%)|1964-2013|WB-GFD<br>Database|
|hhcredit|Total credit to households from all sectors as a share of<br>GDP (%)|1964-2013|BIS|
|nfccredit|Total credit to non-financial corporations from all<br>sectors as a share of GDP (%)|1964-2013|BIS|
|pnfscredit|Total credit to private non-financial sector from all<br>sectors as a share of GDP (%)|1964-2013|BIS|
|institutions|[1.5*_bureaucracy_+_corruption_+_lawandorder_]/3<br>_bureaucracy_: Bureaucracy Quality Index (4: highest quality,<br>0: lowest quality)<br>_corruption_: Corruption Index (6: least corrupted, 0: most<br>corrupted)<br>_lawandorder_: Law and Order Index (6: best , 0: worst)|1984-2013|ICRG|
|initialgdppc|Log of GDP per capita in previous period (constant<br>2005 thousand USD)|1964-2013|WDI|
|financ.open.|Total foreign assets plus total foreign liabilities of the<br>country as a share of GDP|1970-2011|Lane and<br>Milesi-<br>Ferretti<br>(2007)|

30
Since the proposed taxfree amounts will apply to all taxable incomes, a larger share of the benefits are expected to benefit households at the top of the distribution, pointing to efficiency gains that could be possible if the proposed tax break were better targeted. As shown in Figure 11, the poverty and inequality reducing impact per zloty spent is

|Figure 11. Inequality and Poverty-reducing efficiency Family<br>andTax-free allowance|500+|
|---|---|
|Family 500+ & PIT<br>allowance at PLN<br>Family 500+ & PIT<br>allowance at PLN||
|Family 500+<br>5000<br>8000||
|PLN 22.2<br>1.2<br>0.14<br>ine||
|PLN 29.7<br>PLN 41.2<br>0.0<br>0.2<br>0.4<br>0.6<br>0.8<br>1.0<br>0.00<br>0.02<br>0.04<br>0.06<br>0.08<br>0.10<br>0.12<br>Change in Gini relative to Basel<br>(per PLN spent)|Change in extreme poverty<br>(per PLN spent)|
|Redistributive efficiency<br>Poverty-reducing efficiency||
|Source: WorldBankestimates based on HBS2014.||

lower for scenarios that include the tax-free allowance compared to the Family 500+ program alone. About 48 percent of the foregone revenue will benefit households in the top 40 percent of the distribution, while only 30 percent will benefit the bottom 40 percent. In relative terms, the tax break represents 4 to 7 percent of disposable income for the poorest 40 percent, but only 2 to 4 percent for the top of the distribution. These results point to potential efficiency gains by restricting the program to households at the bottom of the distribution.

How do the changes above affect the net cash position of households before and after the reforms? Households in the second decile will now receive more in transfers than they pay into the system through direct and indirect taxes and contributions largely due to the Family 500+ program, although all households will have a net gain (Figure 12). Going forward, it will be important to consider how these initiatives will be financed, and the potential distributional impact of measures needed to ensure that the government is able to keep to its deficit rule.

30
**Table 2: Panel Integration and Cointegration Tests**

# **Part I**

||(s/Y)t|Iny,|r,|flit|_nt_|govst|
|---|---|---|---|---|---|---|
|without trend|||||||
|P=0|-0.617|2.623|-4.152|7.165|-3.495|-0.141|
|P=l|-0.458|1.300|-3.393|5.090|-3.300|-0.859|
|with trend|||||||
|P=(C|0.027|1.213|-3.707|4.676|-3.517|-0.589|
|P=l|-1.645|-0.302|-4.076|3.491|-3.892|-2.014|

Part ll cointegrating vector 1: Ks y),, ln y,, _r,, flit,_ **_,if,_** _govs,]_ cointegrating vector 2: [(s/y),lnyt,fl _,_ govst

|Panel conintegration test|cointegrating vector 1|cointegrating vector 2|
|---|---|---|
|ADF t test|||
|(Pedroni)|-2.27|-2.74|
|Panel ADF|-4.677|-4.95|
|(Im, Pesaram, Shin on residuals)|||

# Notes:

1. P denotes the number of lags in the country specific ADF test.

2. The Panel Integration test is based on Im, Pesaran, Shin (1995). The test is distributed as N (0,1). The unit root hypothesis is rejected at the 5% significance level for values below -1.645.

3. The panel ADF t test is based on Pedroni (1997, a b). The test is distributed as N (0,1).

42
5 survey reports each year on a range of variables that form the basic data set. Questions are asked on subjects pertaining to the household and to individuals within the household. Information is recorded on the geographic location of the household; characteristics of the dwelling; household size; relationships between individuals in the household; activities of individuals; income from labor, transfers and other sources (such as land rents and capital); occupation and other labor characteristics; age; gender; education; ethnicity and literacy. The definition of income throughout the main analysis is gross monthly household income per capita and the population is all individuals in the population.<sup>7</sup> Monetary amounts are all measured in 2004 Brazilian Reais, with a dollar exchange rate of BR$/US$ = 2.89.<sup>8</sup> The Brazilian _INPC_ official consumer price index, which is listed in Appendix 1, is used to convert current incomes into real incomes. For a more detailed description of the data set and methodology see Litchfield (2001).

1 _n y_ Entropy (E) class of measures, _E_ (0) = ∑ log , also known as the mean log deviation or _n i_ = 1<sup>_y_</sup> _i_ Generalised Entropy class of measures is chosen because its members satisfy all of the desired axioms of inequality measures<sup>9</sup> . Whilst the Gini will only satisfy one of these principles under certain conditions, it is included in the analysis to allow some degree of comparability with other

> 7 In this paper, we do not deflate the raw PNAD incomes by a regional price index, nor do we impute rents for owner-occupied housing, since the assumptions required about the stability of certain estimated relationships over 23 years were deemed too strong. The consumption surveys which could be used to generate nationwide regional price indices, for instance, are so far apart (1975 and 1996), as to make sensible comparisons of regionally deflated data over the period we are concerned with in this paper hazardous. See, however, Ferreira et. al. (2003) for results when these adjustments are carried out for a single point in time.

> 8 This is the average nominal exchange rate for the survey reference month, September 2004.

> 9 These axioms are as follows: anonymity; the Pigou-Dalton transfer principle; scale invariance; population replication invariance; and decomposability (see Cowell, 1995).
# **4.1 Data**

To test the model, we employ a survey conducted by the Central Bank of Italy, which gathers information about the innovative activity of Italian manufacturing firms during the periods 1998-2000 and 2001-2003. The data-set collects information for up to 4,660 and 3,452 firms during each period, respectively. A sub-sample of 1,634 companies has been followed since 1998 until 2003. The survey offers a particular good ground to test the theoretical findings, as it provides information on the types of innovations that firms decide to carry out. It also records business perceptions on the impact of imitation on enterprise performance.

## **4.1.1 Proxies**

To test the model, we need to construct a proxy for the quality-bias of technical change. We also need a measure of the (quasi) elasticity of substitution between products, _σ_ , and a proxy for the intensity of the imitation activity, _m_ . The following paragraphs describe the proxies we construct for such purpose.

## **Quality-bias of technical change**

_<u>Qt</u>_ To build a proxy for _Nt_<sup>,weemploythetheoreticalassumptionaccording</sup> to which the leading-edge quality grows over time as a result of knowledge spillovers in the vertical activity, which are a function of the number of firms that up-grade the quality of the existing products. Thus, our proxy is defined as the ratio between the number of firms that in a sector innovate to improve a good, relative to those that innovate to create new varieties. The survey gathers information about the size of each innovation: low, medium, and high. However, we restrict our analysis to high inventions, because they are the most correlated to R&D expenditures. We calculate our measure of the quality bias of technology for each 3-digit ATECO-91 sectors, and we focus on sectors that have more than 5 firms. We do so, to avoid having to employ the Cox transformation for sectors with no innovation, which is problematic as it delivers outlier values.

## **(Quasi) Elasticity of Substitution**

13
Machine Learning, in particular, have the highest share of connected firms through the supply chain network, shown by Table 6.

Table 5. Pioneer supply chain linkages summary statistics

|Gl<br>|obal<br>|||US<br>||
|---|---|---|---|---|---|
|Matched Firm Type|Number|Percentage|Matched Firm Type|Number|Percentage|
|Total|107981|100%|Total|43534|100%|
|Pioneer frm|24261|22.5%|Pioneer frm|24261|56%|
|Supplier|7806|7.2%|Supplier|4846|20.0%|
|Buyer|6202|5.7%|Buyer|4020|16.6%|
|Affliate|18709|17.3%|Affliate|18240|75.2%|
|Innovation partner|4815|4.5%|Innovation partner|3052|12.6%|
|Competitor|9109|8.4%|Competitor|6169|25.4%|

Notes: The table on the left displays the number and percentage of firms in the pioneer cities, and the suppliers, buyers, affiliates, innovation partners and competitors that are linked to them, as a result of matching the Lightcast data with FactSet, across 17 countries from 2014 to 2022. The table on the right lists the same variables for the US alone.

Table 6. Percentage of all firms linked to a pioneer firm by technology and network

|||Single|linkage|Ii|M|ultiple linkag|es|
|---|---|---|---|---|---|---|---|
|Technology|Buyers|Suppliers|Affliates|nnovaton<br>partners|2 linkages|3 linkages|4 linkages|
|Autonomous Cars|0.55%|0.21%|0.13%|0.13%|0.11%|0.03%|0.00%|
|Bispecifc Monoclal Antibody|0.64%|0.33%|0.42%|0.59%|0.27%|0.18%|0.03%|
|Cloud Computing|0.53%|1.25%|0.62%|0.72%|0.36%|0.22%|0.03%|
|Computer Vision|0.81%|1.68%|1.03%|0.99%|0.55%|0.33%|0.08%|
|3D Printing|0.44%|0.68%|0.15%|0.45%|0.24%|0.12%|0.01%|
|Extremely High Frequency|1.00%|1.36%|0.45%|0.88%|0.53%|0.29%|0.04%|
|Fingerprint Sensor|0.78%|1.76%|0.92%|0.99%|0.51%|0.33%|0.07%|
|Fracking|0.07%|0.10%|0.06%|0.04%|0.03%|0.00%|0.00%|
|Hybrid Vehicle_\_Electric Car|0.20%|0.03%|0.02%|0.01%|0.01%|0.00%|0.00%|
|Machine Learning_\_AI|1.17%|2.52%|1.64%|1.29%|0.73%|0.46%|0.06%|
|Mobile Payment|0.61%|0.99%|0.49%|0.63%|0.35%|0.22%|0.04%|
|Smart Devices|0.40%|0.66%|0.47%|0.42%|0.24%|0.13%|0.02%|
|Social Networking|0.44%|0.85%|0.41%|0.49%|0.25%|0.16%|0.02%|
|Virtual Reality|1.02%|2.25%|1.20%|1.32%|0.69%|0.43%|0.09%|
|Wireless Charging|0.84%|1.36%|0.39%|0.97%|0.51%|0.31%|0.05%|

Notes: The table displays the percentage of firms that are linked to a pioneer firm for each technology that emerged since 2005 through supply chain relations. Percentage is calculated over the total number of firms (107,981). Multiple linkages are allowed since firms can have multiple networks with pioneer firms at the same time and for the same technology (e.g., buyer and supplier; supplier, buyer and innovation partner; etc.).

Since the Factset dataset begins only in 2003, we restrict the supply chain analysis to 15 technologies that emerged since 2005, and use the 2 years prior to the year of emergence of each technology to capture pre-existing firm-to-firm linkages.<sup>14</sup> Similarly to the pioneer

> 14The resulting 15 technologies are: Social Networking, Cloud Computing, Smart Devices, Machine Learn-

22
<!-- Start of picture text --> 400 2.E+08 90<br>350 2.E+08 80<br>300 1.E+08 70<br>250 1.E+08 60<br>200 1.E+08 50<br>150 8.E+07 40<br>100 6.E+07 30<br>50 4.E+07 20<br>0 2.E+07 10<br>2003 2004 2005 2006 2007 0.E+00 0<br>02-Jan-03 15-Jan-04 16-Jan-05 23-Jan-06 01-Feb-07 10-Feb-08<br>Avg. number of transactions Avg. transaction-weighted real price Volume traded (left) Average price (right)<br><!-- End of picture text --> _Source: Bloomberg; World Bank staff analysis_

# _3.2.2 Net listing/delisting activity: Strong delisting trend brings number of listed firms back to expected levels_

Despite overall healthy aggregate activity levels, many EGX stocks remain illiquid. To address this, the CMA tightened disclosure and trading requirements in 2002 which started a strong delisting trend. This trend has been further accentuated the removal of tax benefits of listing and M&A activity: almost two out of three companies have delisted since 2002. As a result, the ratio of traded to listed firms has improved from 59 percent in 2005 to its current level of over 75 percent. In 2008:H1, the number of listed firms came down to 375 from a 2002 peak of 1,151, making Egypt comparable to Peru in terms of the number of firms per 1,000,000 inhabitants, but it is still well ahead to Philippines, Morocco, and Pakistan (Exhibit 3.5). Hence, Egypt may have further scope for delistings. The 2006 level seems to be more in line―if slightly higher―with expectations when country characteristics are taken into account. In 2008, the number is 4.9, which is almost exactly as one would statistically expect.

25
|Constant|-2.741***<br>(0.410)|(0.158)<br>2.141**<br>(0.862)|(0.176)<br>1.631*<br>(0.949)|1.525***<br>(0.279)|(0.107)<br>-2.986***<br>(0.735)|(0.125)<br>-3.090***<br>(0.827)|
|---|---|---|---|---|---|---|
|Observations|5207|4632|4111|5207|4632|4111|
|Work conditions|N|Y|Y|N|Y|Y|
|Wages|N|Y|Y|N|Y|Y|
|Region FE|Y|Y|Y|Y|Y|Y|

<u>Source: Authors’ estimation using RLMS 2016 Note: Columns (3) and (6) exclude self-employed informals.</u> This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their level of job dissatisfaction, controlling for other factors. The dependent variable is measured as a binary variable equal to one if the worker is either dissatisfied or strongly dissatisfied with her job (“Dissatisfied with job”), or either satisfied or strongly satisfied with her job (“Satisfied with job”). Omitted categories are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10), hours worked (<40 hours). Armed forces are dropped from the sample. Region dummies are included in all specifications. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., selfemployed).

57 Informal Employment and Worker’s Wellbeing in Russia / June 2019
"Decomposition of variance using a series expansion is another method used for assessing the relative contribution of volume and unit value instability to total value instability (Hazell. et al.). However, the use of this technique requires a well-specified statistical model.

'<sup>4</sup> Identifying the source ot' expo-t earnings instability is important. Stabilization policies often focus attention on price-risk, which in the case of a small-country exporter is exogenous. Price-based stabilization policies include buffer stocks and forward pricing mechanisms. If the instability of export earnings is primarily due to the instability of export volume. then price-based stabilization policies will not be effective in reducing export earnings instability. Export volume instability, or yield-risk can be exogenous (i.e.. due to weather, pestilence), or endogenous (i.e.. due to policy distortions, or the inefficiency of infrastructure and support services). Obviously, the source of yield-risk will have a major impact on the design of appropriate policies.

"I'f PVPRICE and PVEARN give similar results then the country's instability-reducing export portfolo could be calculated based solely on international commodity price movements. If PVPRICE and PVEARN give different results then the country's pattern of domestic production and exports are offsetting international price movements and need to be considered in the stabilization scheme.

"MThe break at 1973 divides the data into 14- and 13-year periods, and marks the major world economic upheavals that took place in 1973-1974.

"The computerized database is based on data teom the FAO Trade Yearbook and the FAO Production Yearbook. Thanks to Ms. Marcelle Thomas of IFPRI who provided this data on diskettes.

41
|**Table 5: Cost Effectiveness Analysis**||**In CFAF**|||**In USD**||
|---|---|---|---|---|---|---|
||**Group 1**|**Group 2**|**Group 3**|**Group 1 **|**Group 2 **|**Group 3**|
|**Program costs:**|||||||
|**Total Program costs**|**21 304 850 **|**154 397 653 **|**195 493 401**|**35 746 **|**259 056 **|**328 009**|
|_Costs by intervention:_|||||||
|One‐stop‐shop for formalization|6 325 293|18 975 879|25 301 172|10 613|31 839|42 452|
|Interventions to increase take up|14 979 557|135 421 774|170 192 229|25 133|227 218|285 557|
|_Costs by types:_|||||||
|Total set up costs|5 728 222|36 001 489|45 733 290|9 611|60 405|76 734|
|Total variable costs|15 576 628|118 396 164|149 760 111|26 135|198 651|251 275|
|**Cost per formalization**|||||||
|Number of businesses|301|899|1199|301|899|1199|
|_Program impact:_|||||||
|Impact on formalization (in pp)|9,6%|13,0%|16,3%|9,6%|13,0%|16,3%|
|Number of firms which formalized|29|117|195|29|117|195|
|because of the program|||||||
|_Total costs..._|||||||
|... per business included in treatment|70 780|171 744|163 047|119|288|274|
|... per formalization|737 294|1 321 106|1 000 289|1 237|2 217|1 678|
|_Variable costs..._|||||||
|... per business included in treatment|51 750|131 698|124 904|87|221|210|
|...per formalization|539 058|1 013 059|766 283|904|1 700|1 286|
|**Cost per formalization with targetting (s**|**ee Table 4)**||||||
|_Targeting firms that looked more like for_|_mal firms befo_|_re head (18%_|_of firms)_||||
|Impact on formalization (in pp)|11,9%|20,7%|19,7%|11,9%|20,7%|19,7%|
|Variable costs per formalization:|434 871|636 220|634 031|730|1 067|1 064|
|_Targeting firm owners with secondary ed_|_ucation (41%_|_of firms)_|||||
|Impact on formalization (in pp)|15,0%|18,2%|22,2%|15,0%|18,2%|22,2%|
|Variable costs per formalization:|344 997|723 613|562 631|579|1 214|944|
|_Targeting firms outside Dantokpa, with a_|_male owner,_|_with seconda_|_ry education. (_|_16% of firm_|_s)_||
|Impact on formalization (in pp)|17,5%|27,7%|29,1%|17,5%|27,7%|29,1%|
|Variable costs per formalization:|295 712|475 443|429 224|496|798|720|
|_Targeting firms with a bank account. (22_|_% of firms)_||||||
|Impact on formalization (in pp)|20,0%|23,7%|22,3%|20,0%|23,7%|22,3%|
|Variable costsper formalization:<br>Notes: 1 USD = 596 CFAF (exchange rate o|258 748<br>n June 1st, 20|555 686<br>16). See Tabl|560 108<br>e A6 for more|434<br>details on|932<br>program|940<br>costs.|

37
cleanest fuel, piped natural gas, is nearly identical to the pattern for ambient (outdoor) pollution. Baseline indoor pollution is set by ambient pollution, which varies nearly twelve-fold (from 30 to 350 ug/m<sup>3</sup> ; mean 113) over the 24-hour cycle. In areas with heavy biofuel use, our results therefore suggest that health benefits may be less than expected for households that switch to clean fuels or improved stoves.

Our survey also suggests that limited information may be a significant impediment to adoption of cleaner, more efficient stoves. Only 15% of our sample households regard improved stoves as a viable option, either because they have not heard of them or because they do not think they are locally available. Even among families that have considered the option, however, improved-stove use appears quite limited because of concerns about convenience or initial investment cost. The intermediate-term prospects for clean-fuel use appear more hopeful in urban and peri-urban areas, if economic growth continues. Our sample evidence suggests very high adoption rates among families whose daily per capita incomes exceed $2.00. However, only 30% of extreme-poverty households (less than $1.00/day per capita) use clean fuels, even in urban areas where their prices are relatively low. In rural areas, our evidence offers little hope for adoption of clean fuels in the near future because their prices relative to biofuels are too high.

However, our analysis also suggests that poor families may not have to wait for clean fuels or clean stoves to enjoy significantly cleaner air. Within our sample household population, some arrangements are already producing relatively clean conditions, even when “dirty” biomass fuels are used. Since these arrangements are already within the means of poor families, the scope for cost-effective improvements may be larger than is commonly believed.

2
<!-- Start of picture text --> Figure 4: Growth is Good for the Poor<br>0.2<br>0 0.15 *,<br>0~ Lo<br>E o / -0.1<br>0<br>Average AuCnilPCiIo0.05<br>_ ~ ~ ~ / ~ ~ *<br>0 4~~~~~~~~~~.*<br>pi-0.2 fe -0.15 a -0.a1 ia -0.la 0.1 0.15 0.2<br>.9, ,<br>developing * P y 1. ~~~~~~~~~~~~~~~~~~~2=I 85x - 0.0068<br>R 0.4935<br>-0.15<br>-0.2<br>Average Annual Change In log(Per Capita Income)<br><!-- End of picture text --> Notes: Figure shows average annual growth rates of indicated variables over noni-overlapping periods of at least five years, in a sample of 285 observations covering 92 developed and developing countries. Per capita income growth refers to real per capita GDP growth. Per capita income growth in the poorest quintile is equal to per capita income growth plus growth in the income share of the poorest quintile.

> Source: Dollar and Kraay (2001)

3 1