rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 14. Asset prices under NIRP**

# **A. House prices**

<!-- Start of picture text --> Index, 2010Q1=100<br>160 Euro Area Sweden Japan<br>150 Switzerland Denmark<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>C. Equity prices<br>Index, 100 = Jan 2012<br>300<br>Euro Area Sweden<br>250 Japan Switzerland<br>Denmark<br>200<br>150<br>100<br>50<br>0<br>E. Share of government debt trading at<br>negative yields over time<br>Percent Europe Japan<br>80<br>60<br>40<br>20<br>0<br>Mar-10 Mar-11 Mar-12 Mar-13 Mar-14 Mar-15 Mar-16<br>Jan-12 Jan-13 Jan-14 Jan-15 Jan-16<br>Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16<br><!-- End of picture text -->

# **E. Share of government debt trading at negative yields over time**

# **B. House price to income ratio**

<!-- Start of picture text --> Index = 100 in 2010<br>Latest<br>120 Global median<br>80<br>40<br>0<br>Germany Sweden Switzerland Japan Denmark France Hungary Spain<br><!-- End of picture text -->

# **D. Price-to-earnings ratio**

<!-- Start of picture text --> Ratio<br>40<br>Euro Area Sweden<br>35<br>Japan Switzerland<br>30<br>Denmark<br>25<br>20<br>15<br>10<br>5<br>0<br>Jan-12 Jan-13 Jan-14 Jan-15 Jan-16<br><!-- End of picture text -->

# **F. Share of government debt trading at negative yields by maturity**

<!-- Start of picture text --> Percent of debt by market value<br>50<br>40<br>30<br>20<br>10<br>0<br>1-3 3-5 5-7 7-10 10+<br>Years to maturity<br><!-- End of picture text --> Sources: Bank for International Settlements, Bloomberg, European Central Bank, MSCI, J.P. Morgan, World Bank. A. Real residential property prices, covering all types of dwellings in the whole country, in both new and existing dwelling markets. Last observation is 2016 Q1 for Switzerland and 2015 Q4 for the rest.

B. Price-to-income ratio, index, 2010=100. Last observation is 2015Q4.

C. Last observation is July 2016.

D. Earnings are trailing twelve month earnings. Last observation is July 2016.

E. Data from the J.P. Morgan Global Developed Market Bond Index. “Europe” includes Belgium, Denmark, France, Germany, the Netherlands, Spain, and Sweden. Includes all bonds in the index with greater than one year maturity. Last observation is May 2016.

F. Includes: Belgium, Canada, Denmark, France, Germany, Italy, Japan, the Netherlands, Spain, Sweden, the United Kingdom, and the United States.

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declined substantially for adult men (135 calories) and women (86) calories over the 1983-2012 period but by less for children (30). Activity levels for adult men and women fell over 6%, and these decreases were only partly offset by their rising REE. For the 1998-1999 TUS, the decrease in AL is equivalent to about one third of the urban-rural gap for adult men and one half for women.

Although the estimated decline in AL for adult men and women appears plausible and quite large, it is instructive to consider what variables in the NSS-E drive these results. Descriptive statistics for all years using the NSS employment survey are reported in Appendix Table S.5. Agriculture is one of the more physically active industries/occupations and declined considerably as a share of employment, from 60% to 43% as a share of main household industry; however, there was large and partly offsetting increase in construction work, from 3% to 12%, which is also very physically active. Within agriculture, the probability that individuals engage in specific manual labor intensive agricultural task in the previous week has only fallen slightly, e.g. 12% to 9% for harvesting activities, 2% to 1% for plowing. The share of women engaged mostly in domestic duties remains very high. Although some of these duties are very physically active, most have declined only slightly, with the largest effect coming from the decline in the probability of bringing water from outside the household premises from 11% to 5%, smaller effects coming from decreases in food processing and animal husbandry, and no change in the probability of collecting firewood or cooking/heating fuel (around 7% throughout the period). The predictive equations used adopt a more flexible specification, but the linear estimates from Table 2 are also instructive. The 3.3 year increase in years of schooling for adults and the over 50% increase in real per capita expenditure imply only a modest decrease in AL over this period (about 3%) for demographically similar households, with a small offset from the decrease in average household size from 6.3 to 5.3 individuals.

The fact that TEE is falling for adult men, adult women and children yet rising for the population reflects the important role played by populationlevel demographic change. The share of children aged 14 and under in the population fell from about 40% in 1983 to 30% by 2012. This implies considerably higher TEE, not just because children are physically smaller and have lower REE, but because children are also much less active than adults according to the TUS estimates, particularly in rural areas. In 1983 child TEE was about 60% of adult TEE, implying that the demographic shift over 11
50% less wood in laboratory tests (GIZ, 2011), 40% to 50% based on surveys (Megen Power, 2008; Dresen et al., 2014), and 20% to 30% less in field-based controlled cooking tests (Gebreegziabher et al., 2018). The Climate Resilient Green Economy strategy of the Federal Government of Ethiopia (FDRE, 2011) proposes to distribute ICS that will be used by about 20 million households by 2030. Reducing the demand for biomass by increasing fuel efficiency is indeed currently one of the strategic priorities in the Ethiopian energy sector (FDRE, 2014).

Our paper builds on previous work based on a randomized controlled trial rolled out in 2013 and examines the effects of the Mirt ICS promoted in Ethiopia on HAP in cooking areas, measured as 72-hour mean, median and maximum PM2.5 concentrations. We also investigate the types of households that receive HAP benefits. In previous papers we have reported on satisfaction and permeal fuelwood savings (Gebreegziabher et al., 2018), actual in-field usage (Beyene et al., 2015b), and learning (Bluffstone et al., 2017). This paper extends the analysis to HAP, using a cross-sectional randomized controlled trial (RCT) implemented in 2016.

We find average, maximum and median PM2.5 concentrations that are very much in line with the literature (e.g. MacCracken and Smith, 1998; Chen et al., 2016), and several orders of magnitude greater than the WHO guideline concentration values. Our analysis suggests that the Mirt ICS on average has limited effects on HAP, with average of mean concentrations only 10% lower in households randomly assigned to receive a Mirt stove. Digging deeper, we echo findings of Langbein et al. (2017) that cooking location matters for ICS results. We find large reductions in HAP, including average mean reductions of 64% and median reductions of 78%, compared with those who did not receive Mirt stoves, for households that have their cooking/baking areas in their main houses;<sup>1</sup> for these households, adoption of the Mirt ICS may offer significant health benefits.

We analyze the characteristics of these households and find they tend to be poorer by several measures, live farther away from all-weather roads, and are more likely to cook with less preferred, smoky fuels like animal dung and agricultural wastes than households who have a separate cooking and baking area. Though our study design does not allow us to identify the reasons for the large percentage reductions in HAP due to Mirt use by households who cook/bake in their main living areas, they may reflect the decreased emissions made possible by greater stove energy-efficiency and

> 1 Though many kitchen areas are distinct rooms for the purpose of cooking and baking, many households combine cooking/baking with living space in small houses.  To avoid giving the impression that cooking/baking areas are always “kitchens” in the sense of separate rooms, we generally use the term “cooking/baking areas” rather than “kitchen.”

3
# **Figures and Tables**

Figure 1: Argentina’s wealth tax rates have ranged from 0.25% to 2.25% <!-- Start of picture text --> Millions of<br>Tax Rates (%) 2015 pesos<br>2.25% 2<br>18m<br>1.75%<br>Exemption 6.5m 306m 1.5<br>cutoff (right) Tax rates<br>1.5% on foreign<br>assets 106m<br>1.25%<br>Cutoff: 5m 20m 24m 1<br>1%<br>Cutoff: 100k Cutoff: 2m 8.5m 12.5m<br>0.75%<br>Cutoff: 200k Cutoff: 750k 800k 5m 9m<br>0.5<br>0.5%<br>Cutoff: 102.3k Cutoff: 102.3k Cutoff: 305k 950k 2m 6m<br>0.25%<br>1.05m<br>0 0<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> _Notes:_ this figure plots the wealth tax schedule in Argentina between 1991 and 2022, showing sizable variation in the exemption cutoff and bracket schedule. The left axis plots the statutory wealth tax rates and associated bracket cutoffs in current pesos. Because these cutoffs are nominally defined, high-inflation spells cause ‘bracket creep’: the exemption cutoff (plotted in the right axis and expressed in millions of 2015 pesos) dropped between 2007 and 2015. Moreover, Argentina’s wealth tax rates have historically ranged from 0.25% to 2.25%, with reforms taking place in 1995, 1999, 2007, 2016, 2019, and 2021. For instance, in 2016 Argentina replaced the bracket schedule based on four ( _average_ ) tax rates with a single ( _marginal_ ) rate of 0.75% and raised the filing threshold. Besides the exemption threshold, the tax has a filing threshold for people with annual gross income above the following thresholds (in pesos): 2007–14: 96k; 2015: 200k; 2016: 500k; 2017: 1m; 2018: 1.5m; 2019: 2m; 2020: 2.5m; 2021: 3.7m; 2022: 6.6m. Argentina applies differential tax rates on foreign assets since FY 2019. To determine the tax rate on foreign assets, taxpayers must first sum domestic and foreign assets and then apply the corresponding ( _average_ ) rate on the total value of foreign assets. Domestic rates apply on total foreign assets when taxpayers repatriate at least 5% of those assets. _Source:_ authors’ compilation based on Ministerio de Economía (2022).

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**Table 4 Top 20 Most Protected Import Categories, by average applied tariffs**

|Sector (ISIC-3 digits)|Imports<br>from EU<br>($mill)|Share of<br>Imports<br>from EU|Average<br>Tariff Applied<br>on Imports<br>from EU|
|---|---|---|---|
|160- TobaccoProducts|9,163|0.11%|43.3%|
|153-Grain Mill Products, Starch Products, and Prepared Animal Feeds|62,004|0.72%|22.0%|
|293- DomesticAppliancesn.e.c.|26,764|0.31%|20.4%|
|333- watches and clocks|230|0.00%|20.2%|
|202-Products of Wood, Cork, Straw and Plaiting Materials|15,085|0.18%|17.8%|
|50- Fishing andrelated activities|3,469|0.04%|16.8%|
|252 - PlasticsProducts|69,678|0.81%|16.0%|
|141-Quarrying of Stone, Sand and Clay|8,066|0.09%|15.9%|
|269- Non-metallicMineral Productsn.e.c.|156,562|1.82%|15.8%|
|261 -Glass and GlassProducts|15,618|0.18%|15.2%|
|154-Other Food Products|229,174|2.67%|14.1%|
|289-Other FabricatedMetal Products;|89,140|1.04%|13.9%|
|192 - Footwear|2,355|0.03%|12.8%|
|172-Other Textiles|9,492|0.11%|12.5%|
|201 -Sawmilling andPlaning of Wood|165|0.00%|12.1%|
|251 - Rubber Products|65,176|0.76%|11.6%|
|191-Tanning and Dressing of Leather; Luggage, Handbags etc|1,273|0.01%|11.5%|
|341 - Motor Vehicles|593,116|6.91%|11.3%|
|369- Manufacturingn.e.c.|12,087|0.14%|11.3%|
|151 - Production,Processingand Preservation of Meat,Fish,Fruit,|329,790|3.84%|10.7%|

Source: Computed by authors based on TRIST model and database In the absence of detailed information regarding the likely nature of an EPA between Nigeria and the EU, assumptions have to be made when designing the policy experiment to be simulated. Given that compatibility with Article 24 of the GATT only requires a given FTA to cover “substantially all trade” and following the precedence of the already signed “interim EPA,” the central policy experiment reported in this study assumes that 20 percent of Nigeria’s imports from the EU are excluded from the EPA liberalization schedule, this is the working assumption that the EU has adopted in its negotiations for free trade agreements. For simplicity, it is conjectured that the sensitive (or exclusion) list is chosen according to degree of protection that each product (imported from the EU) receives.<sup>10</sup> This would provide large room to maneuver for the Nigerian government and implies that a large fraction of imports from the EU would not be liberalized for a number of sectors (Table 5). For instance, the tobacco sector would be

> 10  Concretely, products are ranked by collected tariff (on imports from the EU) and then starting from the top of the list, products are excluded until a cumulative import value of 20% is reached.  This criterion should by no means be interpreted as a policy recommendation for the definition of a sensitive product list. In practice, the definition of sensitive products should depend on a number of other factors, including the poverty, employment and environmental impact of liberalization.

12
measures aggregated at the country level. Figure 6 presents the main results. In countries with high ratios of private credit to GDP firms are less likely to be fully or partially credit constrained (FCC or PCC) and more likely to be non-credit constrained (NCC) or maybe credit constrained (MCC). These relationships are stronger in the two extremes of the ordinal variable (NCC and FCC) as shown by the larger slopes of the respective linear approximation.

# **5. Comparison of the Ordinal Metric of Credit-Constrained to Other Accessto-Finance Indicators**

In the literature, access to finance can refer to the availability and use of different financial instruments. Some papers define access to finance based on only one condition: whether or not a firm has a loan (Banerjee and Duflo 2004), or the stock of liquid assets (Love 2003), external financing without loans (Klapper, Laeven, and Rajan 2006). Still other papers may use more than one financial instrument to describe access to finance: Honohan (2007) define access to finance using bank loan and deposit accounts.

These differences in the literature illustrate the advantages of Enterprise Surveys which contain information on a detailed list of a firm’s participation with financial instruments and services. The credit constraint status of a firm is computed using information on the sources of external financing for working capital and investment, whether or not a firm applied for a loan, has a loan, and the reason they did not apply for a loan.

Table 8 displays results from logit regressions where the dependent variable varies in the use of financial instruments. The regressions are estimated to determine the relationship of these dependent variables to labor productivity and to determine how they compare to the ordinal metric constructed in this paper. The results of the ordinal metric, subsumed into a dummy 18
the firm level is important for explaining the dynamics of wage inequality at the country level.

In this paper we analyzed the relation between ICT adoption and labor demand of white and bluecollar workers and the corresponding changes in terms of the wage gap between these two groups for the case of Mexico between 2008 and 2013 using a firm-level panel of manufacturing, services and commerce firms from the two last Economic Censuses. Our results for the manufacturing sector indicate that ICT adoption has indeed increased the labor demand of more highly skilled workers, approximated by white-collar workers relative to blue-collar workers, which is indicative of firms upgrading their skill mix due to ICT adoption. However, similar effects are not observed in terms of the wage gap between these two groups as both of them exhibit wage increases and furthermore, as the effects of ICT on the wage gap are negative.

A possible explanation for these results, which is supported by the analysis of ICT related variables obtained from a detailed survey regarding ICT for Mexico is that blue-collar workers are becoming more sophisticated as a result of an increasing availability of information within the firm due to a more intensive use of ERP systems as well as an increasing training of blue-collar workers in these technologies.

Other alternative explanations could be associated to rent-sharing mechanisms as explained in Brambilla (2016) or to the job polarization literature in which as explained in Michaels et al. (2014) for medium-skilled workers the effects of ICT on wages are negative against low-skilled workers. In this sense, the presence of medium-skilled workers among white-collar and blue-collar workers could lead us to underestimate the real effect on the wage gap between high-skilled and low-skilled workers. However, to appropriately test this last hypothesis we would need more detailed information regarding tasks and education of workers which could only be obtained through an employee-employer matched database, which is not available in the case of Mexico.

It is also important to note that heterogeneity is observed in terms of sectors as even though the services sector also shows a decrease in the wage gap between white and blue-collar workers, this result has a different explanation as wages for both groups decreased on average as a result of ICT adoption. On the other hand, for the commerce sector, an increasing ICT use is associated with a higher wage gap between these two groups.

Our results are important in terms of their policy implications for the case of a developing country such as Mexico. Contrary to the predictions of the SBTC literature, the number of unskilled workers proxied by blue-collar workers and their wages are not decreasing as a result of ICT adoption, but instead this kind of workers are becoming more sophisticated and increasing their use of these technologies therefore improving their wages. In this sense, activities aimed at promoting ICT use can be effective not only to enhance firm-level productivity, but also as a mechanism to reduce wage inequality.

59
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<mark>Bethlehem, J., & Stoop, I. (2007). Online panels. A paradigm theft?</mark> _<mark>The challenges of a changing world</mark>_ <mark>, 113-131.</mark> <mark>Braunsberger, K., Wybenga, H., & Gates, R. (2007). A comparison of reliability between telephone and web-based surveys.</mark> _<mark>Journal of Business Research</mark>_ <mark>,</mark> _<mark>60</mark>_ <mark>(7), 758-764.</mark> <mark>Bruggen, E., J. A. van den Brakel, and J. Krosnick (2016), “Establishing the Accuracy of Online Panels for Survey Research,” Statistics Netherlands. Available at https://www.cbs.nl/en-gb/background/</mark> <mark>2016/15/establishing-the-accuracy-of-online-panels-for-survey-research (accessed September 30, 2019).</mark> <mark>Callegaro, M., Villar, A., Yeager, D. S., & Krosnick, J. A. (2014a). A critical review of studies investigating the quality of data obtained with online panels based on probability and nonprobability samples. Online Panel Research: A Data Quality Perspective, Wiley (2014), pp. 23-53</mark> <mark>Callegaro, M., Baker, R. P., Bethlehem, J., Göritz, A. S., Krosnick, J. A., & Lavrakas, P. J. (Eds.). (2014b). Online panel research: A data quality perspective. John Wiley & Sons.</mark> <mark>Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of human resources, 50(2), 317-372.</mark> <mark>Chang, L., and J. A. Krosnick (2009), “National Surveys via RDD Telephone Interviewing versus the Internet,” Public Opinion Quarterly, 73, 641–678.</mark> <mark>Cornesse, C., Blom, A. G., Dutwin, D., Krosnick, J. A., De Leeuw, E. D., Legleye, S., ... & Wenz, A. (2020). A review of conceptual approaches and empirical evidence on probability and nonprobability sample survey research. Journal of Survey Statistics and Methodology, 8(1), 4-36.</mark> <mark>Dever, J. A., Rafferty, A., & Valliant, R. (2008, June). Internet surveys: Can statistical adjustments eliminate coverage bias? In Survey Research Methods (Vol. 2, No. 2, pp. 47-60).</mark> <mark>Diamond, A., & Sekhon, J. S. (2013). Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies.</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(3), 932-945.</mark> <mark>Duffy, B., Smith, K., Terhanian, G., & Bremer, J. (2005). Comparing data from online and face-to-face surveys.</mark> _<mark>International Journal of Market Research</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>(6), 615-639.</mark> 24
<!-- Start of picture text --> 16%<br>14% poe<br>| ‘.<br>. \ --***Income from Drug<br>12% Trafficking/GDP<br>10% - : .‘ [ - ~ : x ‘N<br>8% -' 7's. = Crime/GDP<br>6%<br>:<br>2% = SS arr — - Stock of Laundered<br>Berm maw’. Vee ~ oom lee<br>mee set” - alee 7 - Stipes Assets/GDP<br>0%<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 # £2013<br><!-- End of picture text -->
# **FIGURE 1 The Growth-Inequality Relationship: Various Endpoints**

<!-- Start of picture text --> 0 .05 .1 .15 .2<br>Village Inequality in 1987-88 (Mean Log Deviation)<br>1990-91 Fitted values 1995-96 Fitted values<br>1997-98 Fitted values 1999-00 Fitted values<br>2001-02 Fitted values<br>.2<br>.1<br>0<br>-.1<br>Average Annual Growth Rate (Household PC Income)<br>-.2<br><!-- End of picture text --> _Notes:_

- 1) The fitted values are based on simple regressions of growth from period 1987-88 through period _T_ , on initial inequality in 1987-88. The regressions are weighted by the number of households in the village sample. The number of villages is 82.

- 2) The regression coefficients (and _t_ -values) are for 1990-91: -0.084 (0.46); for 1995-96: -0.157 (1.87); for 1997-98: -0.215 (2.94); for 1999-00: -0.133 (2.04); for 2001-02: -0.130 (2.30).