rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3.6 Share of Resource Exports in Merchandise Exports and Share of Research and Development Expenditure in GDP in 2015**

<!-- Start of picture text --> 4.5<br>ISR KOR<br>4.0<br>3.5<br>JPN SWE<br>3.0 AUT DNK<br>DEU FINUSA<br>2.5 SVN BEL<br>FRA ISL<br>2.0 CHN CZE NOR<br>GBR<br>1.5 EST<br>SVK LUX ESP MYS LTU RUS<br>1.0 POL BGR GRC<br>MEX HRV<br>EGY<br>0.5 MAC KHM PAK IND CYP OMN IRQ<br>SLV BIH KGZ<br>0.0<br>0 10 20 30 40 50 60 70 80 90 100<br>Source: World Bank, World Development Indicators.<br>Note: Share of resource exports in merchandise exports in horizontal axis; and share of research and<br>development expenditure in GDP in vertical axis.<br><!-- End of picture text -->

**Figure 3.7 Share of Resource Exports in Merchandise Exports and Patent Counts Per Capita in 2015**

<!-- Start of picture text --> 500<br>450<br>ISR USA<br>JPN<br>400<br>350 KOR<br>300<br>SWE<br>250 FIN<br>200 DEU<br>DNK<br>150 ISL<br>AUT<br>NOR<br>100 GBR BEL<br>HKG<br>50 CHN MEX LUXTUNFRA KGZ EGY CYP GRC OMN RUS IRQ<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Source: World Bank, World Development Indicators ; IP5 Offices; and World Bank staff calculations.<br>Note: Share of resource exports in merchandise exports in horizontal axis; and patent counts per<br>capita in vertical axis.<br><!-- End of picture text --> Such within-firm factors as corporate governance are important determinants of whether the oil sector can transform and adapt to the changing reality of global energy markets. The data suggest that there are important differences in the level of innovation when comparing corporations—private versus stateowned. State-owned enterprises lag when it comes to the propensity to innovate. Indeed, to the extent that the abundance of resources tends to direct the technological change toward capital-intensive 11
Table 1. Capital cost used from different sources (US$/kW, 2019 price)

|Data source|Solar PV<br>- Utility|CSP|Wind<br>Onshore|Wind<br>Offshore|Gas CC|Gas GT|Geother<br>mal|Hydro|Coal|Nuclear|Biomass|
|---|---|---|---|---|---|---|---|---|---|---|---|
||||||Minimum|Value||||||
|Lazard|900|6,000|1,100|2,350|700|700|3,950|n.a.|3,000|6,900|n.a.|
|EIA|1,331|7,191|1,319|5,446|1,017|710|2,680|2,752|3,661|6,317|2,831|
|IRENA|618|3,704|1,039|2,677|n.a.|n.a|2,020|680|n.a|n.a|422|
|IEA|1,005|3,831|1,287|3,973|673|536|1,602|1,282|1,072|2,805|630|
|NREL|1,142|6,574|1,678|3,145|944|937|4,557|3,974|3,867|6,460|3,988|
||||||Maximum|value||||||
|Lazard|1100|9,100|1,500|3,550|1,300|950|6,600|n.a.|6,200|12,200|n.a.|
|EIA|1,331|7,191|1,319|5,446|1,017|710|2,680|2,752|3,661|6,317|2,831|
|IRENA|2,794|7,127|2,482|5,551|n.a.|n.a.|7,280|4,138|n.a.|n.a.|8,742|
|IEA|2,750|8,735|3,217|6,365|1,383|1,001|7,108|n.u.|3,290|6,668|9,298|
|NREL|1,142|6,574|1,678|5,318|944|937|n.u.|7,418|4,225|6,460|4,182|

n.a. -- not available because the study does not include the particular technology, n.u. -- not used as the values are outliers.

Note: Some values presented here may not necessarily be the same as provided in the sources because we converted the data from the sources to 2019 prices using US GDP deflators.

Sources: IRENA (2020), Lazard (2019), IEA (2015), EIA (2020) and NREL (2019).

11
39 Table 1.2 Adaption of social security progress (Number of economies with programs In selected years) Lo*-noome economies (31 out of 37 reported som program as of 1965)

|Type of benefit|1925|1945|1965|1985|
|---|---|---|---|---|
|Old age, disability,<br>or death|0|0|17|24|
|Sickness<br>or maternity|0|0|14|14|
|Work injury|4|17|30|31|
|Unemployment|0|0|0|1|
|Family<br>Allowance|0|0|10|10|

Lower middle-income economies (33 out of 36 reported sowa program as of 1985)

|Type of benefit|1925|1945|1965|1985|
|---|---|---|---|---|
|Old age, diubility.<br>or death|1|5|25|30|
|Sickness<br>or maternity|1|7|22|23|
|Work Injury|8|25|30|32|
|Unemployment|0|1|3|6|
|Family<br>Allowance|0|4|10|11|

Upper middle-income economies (23 out of 24 reported some program as of 1965)

|Type of benefits|1925|1945|1965|1985|
|---|---|---|---|---|
|Old age, disability,<br>or death|3|13|20|22|
|Sickness<br>or maternity|6|12|18|19|
|Work InJury|13|21|23|23|
|Unevployment|0|4|5|8|
|Family<br>Allowance|0|6|14|14|

Industrial market economies (19 out of 19 reported some program as of 1985)

|Type of benefits|1925|1945|1965|1985|
|---|---|---|---|---|
|Old age, disability,<br>or death|13|17|19|19|
|Sickness<br>or maternity|12|16|17|18|
|Work Injury|18|19|19|19|
|Vnemploymcnt|12|18|19|19|
|Family<br>Allowance|0|11|17|19|

Note: Gabon has been added to the upper middle-income countries. See note I to Table 1.1.

Sources: Program Information: U.S. Social Security Administration (1986), Social Security Programs Throughout the World 1985, Washington, D.C. Incom categorles: World Development Report 1987, World Development Indicators, Table 1, World Sank, Washington, D.C.-
China’s stated policy overlaps (“R2”) include the RPS as the renewable promoting policy and the scope-2 indirect emission pricing (IEP) for the ETS-covered sectors. In the model, the RPS policy is an endogenous electricity-consumption-tax-funded renewable subsidy, where the rates fulfill the projected RPS targets each year. The RPS target shares for non-hydro renewable electricity at the national level in 2025 and 2030 are drawn from NEA’s consultation draft (NEA, 2021). The 2035 target is projected assuming consistent annual growth rates between 2030 and 2035 using linear trends.

**Table 3. Overlapping Policies Considered**

|**Overlapping Policy**<br>**Cases**|**Renewable Support**|**Electricity Taxes**|
|---|---|---|
|**NO**|None|None|
|**R2**|RPS|RPS+ IEP|
|**RN**|RPS|RPS|
|**S2**|IRS|IEP|
|**SN**|IRS|None|
|**N2**|None|IEP|

Notes: i) Each overlapping policy case can pair with each of the four ETS types, indicated by a hyphen between the ETS type and the overlapping policy case.

ii) NO: No overlaps. R: overlapping RPS set according to projected national targets each year. S: Independent renewable subsidy (IRS) set to meet projected RPS targets each year. 2: indirect emissions pricing (IEP) applied to the electricity consumption of ETS-covered industrial sectors.

Alternative overlapping cases reveal how different elements in the stated policy overlap R2 affect the abatement costs. The RN scenario simply removes the IEP from R2. The S scenarios remove the implicit electricity taxes on all electricity consumers by replacing the RPS scheme with an independent renewable subsidy (IRS), funded by general revenues, that achieves the national renewable share target. S2 would retain the IEP obligation, while SN eliminates it. N2 has no renewable support but retains the IEP.<sup>19</sup> All policies are compared to a reference case with no ETS, renewables subsidies, or electricity taxes. Throughout, the cost per ton of abatement is measured as the present value of the equivalent variation of household consumption in each phase, divided by the cumulative

> 19 Some scenarios defined here for completeness are reported only in extended simulations in the Appendix.

31
inclusion of interest on late payment of duties. The multi-goods variable, which measures the average number of products on declarations containing each _ofkt_ observation, has a mean of 7 _._ 53. One declaration contained 212 HS10 products.

Table 4: Summary Statistics for 2013-16

||N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Shipments|2431641|5.55|22.02|1.00|5848.00|
|Inspections|2431641|0.40|5.64|0.00|2453.00|
|Inspection rate|2431641|7.80|24.72|0.00|100.00|
|Bin_n_=0|2431641|0.54|0.50|0.00|1.00|
|Bin_n_=1|2431641|0.13|0.33|0.00|1.00|
|Bin_n_=2|2431641|0.08|0.26|0.00|1.00|
|Bin_n∈_[3_−_5]|2431641|0.11|0.31|0.00|1.00|
|Bin_n∈_[6_−_10]|2431641|0.06|0.25|0.00|1.00|
|Bin_n∈_[11_−_100]|2431641|0.08|0.27|0.00|1.00|
|Bin_n>_100|2431641|0.00|0.07|0.00|1.00|
|Shipments_t−_1|2431641|4.23|19.94|0.00|5848.00|
|Insp_t−_1|2431641|2.98|14.53|0.00|100.00|
|ln UV_t_|2431615|7.77|2.08|-6.93|22.80|
|AV tarif_t_|2431641|0.07|9.13|0.00|8612.07|
|Multi-goods|2431454|7.53|11.57|1.00|212.00|

Table notes: Summary statistics for Serbian customs data from 2013-16. Annual data organized at the level of HS10 product, origin country and importing firm. _Bin_ variables are dummy variables indicating the frequency of shipments in the product-country-firm triplet in the previous year. Subscripts on the bin dummies indicate ranges of the number of lagged shipments associated with the dummy variable for that bin.

# **Appendix B: Theoretical framework**

## **General Framework**

It is assumed that the inspecting agency’s uncertainty can be described by a non-empty finite set of states, Γ. There is a set of events, _ε_ , associated with the set of states, which are the power set of Γ. It is assumed that for each _γ_ in Γ, _{γ}_ is in _ε_ . Let _X ⊂_ R be a non-empty, finite set of outcomes and let _G_ = _{g_ : Γ _→ X}_ be a simple set of functions from 33
# All imports

Figure 1 OECD import concentration, 1963-2006 Extra-OECD imports only <!-- Start of picture text --> 105<br>120<br>100<br>110 Herfindahl 95<br>Theil<br>90<br>100<br>85<br>90 80<br>75<br>80 Theil<br>70 Herfindahl<br>70 65<br>60<br>60<br>1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 1963196619691972197519781981 198419871990 19931996 199920022005<br><!-- End of picture text --> Note: base 100, 1963; simple averages of indices over all products. Data from COMTRADE As for the extensive margin, Figure 2 shows the evolution of simple and importweighted averages, across SITC4 lines, of the number of exporters to the OECD over the sample period.

Table 2 Regression results, OECD import concentration on time trend

||All im<br>|ports<br>|Extra-OECD i<br>|mports only<br>|
|---|---|---|---|---|
|Regressors:|(1)<br>Herfindahl|(2)<br>Theil|(3)<br>Herfindahl|(4)<br>Theil|
|time|-0.002***|-0.013***|-0.007***|-0.023***|
||(-13.19)|(-34.03)|(-31.80)|(-43.53)|
|timesq|0.000|0.000***|0.000***|0.000***|
||(1.616)|(4.723)|(23.17)|(28.13)|
|Constant|0.218***|2.913***|0.425***|3.330***|
||(202.6)|(861.4)|(221.1)|(686.9)|
|Observations<br>|54030|54030|53769|53769|
|Number of index|1301|1301|1301|1301|
|R-squared|0.571|0.671|0.510|0.570|
|turning point<br>|2001|2001|1993|1997|
|Product FE|yes|yes|yes|yes|

Notes: t statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 The extensive margin as measured by the average number of source countries does not seem to show the same kind of trend reversal that we observed in the concentration indices, which pick up action at both the extensive and intensive 8
minimum wage policies are in place. This is a relevant question in the context of our analysis, as Indonesia enacted a monthly minimum wage policy at the local level over the period of analysis.<sup>41</sup> We test for the hypothesis that a tighter minimum wage policy reduces the impact of product market regulation on markdowns by interacting _nilit−_ 1 with the regency logminimum wage.<sup>42</sup> To estimate both the main impact of regency-level (log) minimum wages and their interaction with _nilit−_ 1, we replace regency-year with province-year fixed effects and control for the base-year local covariates discussed in Section 5.1.<sup>43</sup> The results are presented in column 1 of Online Appendix Table A6. As expected, the coefficient of minimum wages is negative, but it is not statistically significant (coeff. = - .214, s.e. = .170). However, the interaction term is negative and significant. We interpret this evidence to suggest that minimum wage policy is less relevant in unrestricted product markets, where employers’ labor market power is lower. However, in restricted markets where employers have substantial power, minimum wages limit the extent to which labor can be exploited. In column 2, we re-include regency-year fixed effects and estimate the interaction term only. We find a similar but even stronger reduction in the impact of NIL on markdown than in column 1.

Thus, we conclude that minimum wages can significantly limit labor exploitation, even when employers’ market power originates from the product market.

> 41 The minimum wage in Indonesia is set by the provincial governors on the basis of the recommendation by the regional wage council (or the regency’s regent). The council is a tripartite institution comprising members of government, labor unions, and employers. The members negotiate and set the standards of decent cost of living ( _Komponen Hidup Layak_ or KHL in Indonesian), which forms the basis of the minimum wage recommendation to the regency governor. This recommendation is a central element in the governor’s decision on the minimum wage level, along with political negotiations with employers and trade unions.

> 42 While minimum wages are set at the provincial level every year, regencies are allowed to have different rate of minimum wage, as long as it is above the provincial minimum wage. The regency-wage data was kindly shared by Chris Manning and Nurina Merdikawati, who manually collected the data from individual Governors’ decrees. The data is only available for Java and Bali, which however comprise the bulk of our sample.

> 43 The inclusion of province/district-year effects purges the nominal values of the minimum wages from the impact of price growth.

28
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ structural break occurred in the market in about 1975, the year in which Cote d’Ivoire’s share of world output rose to 15 percent (Table 4).

## **Table 4: Cocoa, Coffee and Cotton Market Performance Summary, Real World Price Trends versus Physical Production in Cote d’Ivoire**

|Compou<br>For 1961-1977, 1977-2003, and<br>TonnageProducedinCote d’Iv|nd Average Annual Gro<br>1961-2003 - Raw Com<br>oire VersusInflation A<br>|wth Rates<br>modities: Cocoa, Coff<br>djustedPrices onWor<br>|ee & Cotton<br>ldMarkets<br>|
|---|---|---|---|
||1961-1977|1977-2003|1961-2003|
|COCOA BEANS||||
|Production Growth (Metric Tons)|+8.61%|+4.69%|+6.17%|
|RealWorldPriceTrend|+8.80%|-8.88%|-1.28%|
|COFFEE BEANS||||
|Production Growth (Metric Tons)|+2.86%|-2.28%|-0.34%|
|RealWorldPriceTrend|+6.77%|-4.61%|-3.21%|
|COTTON LINT||||
|Production Growth (Metric Tons)|+19.93%|+6.34%|+11.33%|
|RealWorldPriceTrend|+1.18%|-4.61%|-2.47%|

Sources: Commodity production quantity data is from the FAO, and world prices in US dollars from commodity exchanges are provided by the IMF. The US CPI is used to adjust for inflation.

# **4. Capital, Labor, And Total Factor Productivity**

**The following estimates of the capital stock, human capital, and output per worker are hoped to shed light on the underlying, long-term factors behind Cote d’Ivoire’s growth performance.** We begin by discussing the evolution of the capital stock and then an estimate of the educational attainment of the labor force.

## **Physical Capital Stock**

**Since 1982, Cote d’Ivoire’s capital stock has declined with the short-lived exception of the immediate post-devaluation years (1995-99).** Capital stocks are known to be difficult to measure, flows less so. Our estimate of the capital stock uses the conventional “perpetual inventory method” based on gross capital formation, an initial capital stock and a depreciation rate of 10 percent<sup>11</sup> (Figure 10). According to our estimate, after 1979, the increase in the real capital stock of the entire economy slowed dramatically and since 1982 actually declined, and never recovered.

> 11 For details of the estimate and the sensitivity to initial capital stock and depreciation assumptions, see Annex 1.

12
||**<=10,**|**00,000**|**>10,00,**<br>**<=20,**|**000 but**<br>**00,000**|**>20,00,**<br>**<=30,**|**000 but**<br>**00,000**|**>30,00,**<br>**<=40,**|**000 but**<br>**00,000**|**>40,00,**<br>**<=50,0**|**000 but**<br>**0,000**|**>50,**|**00,000**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**City**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|
|Ahmedabad|2|1|2|1|3|1|0|2|0|0|1|2|
|Pimpri-<br>Chinchwad|0|0|2|3|0|2|41|1|1|1|2|2|
|Bhubaneswar|1|0|3|4|2|0|2|1|0|0|0|3|

Table A5.3: Achieved FGD sample by gender (F=female, M=male), the current value of the property, and city (breakdown of cities with lower value properties)

||||||**Curre**<br>|**nt value o**<br>|**f propert**<br>|**y (INR)**<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**<=10,0**|**0,000**|**>10,00,**<br>**<=20,**|**000 but**<br>**00,000**|**>20,00,**<br>**<=30,**|**000 but**<br>**00,000**|**>30,00,**<br>**<=40,**|**000 but**<br>**00,000**|**>40,00,**<br>**<=50,0**|**00 but**<br>**0,000**|**>50,**|**00,000**|
|**City**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|
|Ahmedabad|1|0|0|2|3|1|3|0|0|2|1|2|
|Pimpri-<br>Chinchwad|0|0|0|0|2|3|3|1|0|2|3|3|
|Bhubaneswar|0|0|1|0|1|3|3|1|0|0|3|4|

Table A5.4: Achieved FGD sample by gender (F=female, M=male), household income and city

||||**Monthly ho**<br>|**usehold incom**<br>|**e (INR)**<br>||
|---|---|---|---|---|---|---|
||**<=50,000**||**>50,000 but <=8**|**0,000**|**>80,000**||
|**City**|**F**|**M**|**F**|**M**|**F**|**M**|
|Bengaluru|3|1|2|4|3|3|
|Ahmedabad|8|5|0|2|0|0|
|Mumbai|3|2|3|2|2|4|
|Pimpri-Chinchwad|8|9|0|0|0|0|
|Bhubaneswar|7|5|1|1|0|2|
|Delhi|6|5|2|3|0|0|
|TOTAL|35|27|8|12|5|9|

44
advice that was _worse_ than that provided by unqualified providers. The results opened up a substantial debate on the practices of the medical sector and the importance of incentives in the profession, a debate that eventually led to an incentive-based design in India’s quest for universal health coverage.

Clearly, the researchers muddled through. DH made several decisions that reflected both their own prior beliefs that the study was critical to understanding the nature of health care delivery in India and to provide information for better policy that would eventually help the poor. Each of the 3 key decisions: “violating” privacy, informed consent only from providers and not using medical personnel could rightly be criticized using strict definitions from human resource subjects courses. Yet, when DH asked the fundamental question “how could this study be done with full privacy, informed consent from every patient and medical personnel as observers”, people who were commenting could offer no alternative; they even heard the view that “such data should not be collected because all the ethical protocols cannot be strictly followed.” Yet, DH were not harming either the patient or the doctor, the data that they collected played (and continue to play) an important role in improving the lives of the poor, and subjects in the research (both doctors and patients) agreed with the study protocols. And, in many cases, patients actively welcomed our research on medical practices. It appears to us that we have little guidance in such situations.

Addressing deeper questions regarding the validity of diagnoses obtained by patients and differences between doctors in the treatment of patients raises further issues pertaining to the ethics of research on survey delivery. Specifically, the DH studies using interactions with real patients were still subject to biases in estimations of practice-quality across doctors due to unobserved case and patient selection. For instance, it could be argued that lower process quality 19
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<mark>Ballon, P. (2023). Statistical issues in multidimensional poverty measurement: redundancy analysis. In: Silber, J. (ed.)</mark> _<mark>Research Handbook on Measuring Poverty and Deprivation</mark>_ <mark>(pp. 463-474). Edward Elgar Publishing.</mark> Barron, P., L. Cord, J. Cuesta, S. Espinoza, G. Larson and M. Woolcock (2023). Social sustainability in development. World Bank: Washington DC.

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29
# **Table A4: Reasons for disruption to normal agricultural activity**

||BFA|ETH|TZA|UGA|
|---|---|---|---|---|
|Lack hired labor|6.6||37.0|3.7|
||(6.36)||(14.15)|(1.69)|
|Lack of seeds|0.0|25.0|12.0|13.2|
|||(6.36)|(8.65)|(5.09)|
|Lack of fertilizer|6.8|55.6|9.7|0.0|
||(4.11)|(7.39)|(6.53)||
|Lack of other inputs|2.1|24.2|1.5|2.8|
||(2.12)|(8.19)|(1.16)|(1.66)|
|Unable to sell outputs|0.0|4.7|0.0|1.1|
|||(3.22)||(0.82)|
|Illness|0.9|1.4|24.8|12.1|
||(0.77)|(1.34)|(12.99)|(4.13)|
|Delayed planting|0.7|0.0|0.0|3.1|
||(0.74)|||(1.40)|
|Climate|40.2|66.9|48.3|84.2|
||(9.48)|(6.14)|(13.68)|(3.54)|
|Pests|1.5|1.4|5.9|5.5|
||(1.48)|(0.77)|(5.82)|(2.09)|
|Insecurity|66.7|7.2|0.0|2.0|
||(9.12)|(2.05)||(1.10)|
|_N_|55|186|24|132|

Note: Reasons for disruption to agricultural activity in most recent round. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

25