rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 5. Work stoppages in Lao PDR, PNG, and Vietnam Lao PDR PNG Vietnam Note: 95% confidence intervals for difference from Q1 are shown (rather than from zero). Including controls for respondent characteristics do not qualitatively change results. See Appendix Table 2 for regression tables with controls. Source: HFPS; Round 3 is used for Vietnam Jul-Sep 2020 Indonesia and the Philippines – Like other countries discussed above, Indonesia and the Philippines experienced a steep decline in mobility early in 2020, but unlike the others, they faced a pandemic-induced health crisis relatively early in 2020 and a prolonged period of depressed mobility (lower than -20 percent). Far-reaching mobility restrictions translated into employment shocks that were similarly widespread across the welfare distribution (Figure 6). A wide range of sectors experienced work stoppages during early stages of the pandemic, including sectors such as retail, transportation, and finance in which significant shares of wealthier workers were employed. For example, in Indonesia, HFPS data indicate that more than half of workers in the top 20 percent of the welfare distribution were employed in retail, transportation, or 14
policy makers currently have little systematic information about targeting performance on which to base their analysis of residential utility subsidies as a utility policy or social policy option.

This article seeks to contribute to closing this gap by systematically examining the targeting performance of residential utility subsidies using data from 13 water utilities and 27 electricity utilities around the world. A conceptual framework that decomposes the determinants of targeting performance is employed to identify those factors that most strongly influence the empirical results and predict targeting performance for various types of subsidies. We begin with an overview of the types of utility subsidies that exist and their prevalence among water and electricity utilities. The article then focuses on the analysis of the benefit and beneficiary targeting performance of consumption and connection subsidy schemes.

The results indicate that the most common forms of residential utility subsidies – quantity-based consumption subsidies such as increasing block tariffs -- are highly regressive. Most poor households are excluded from these subsidies, and the majority of benefits accrue to the non-poor. Prospects for achieving a progressive incidence of subsidies are better when other targeting mechanisms are used, such as means-testing. However, low levels of coverage and metering severely compromise the ability of any consumption subsidy scheme to reach the poor. Where coverage of standard in-house water and electricity service levels is low, subsidies targeted only to consumers of lower-levels of service or connection subsidies are better alternatives, but the performance of these subsidy models is highly dependent on how poor households respond to the subsidy offer. Even connection subsidies and servicelevel targeted subsidies may not reach many poor households if price is not the principal barrier to connection for poor households or if demand for alternative services (such as public taps or low voltage electricity service) is low.

# **Typology and prevalence of subsidies**

We distinguish in this analysis between two broad categories of utility subsidies – consumption subsidies and connection subsidies. Consumption subsidies reduce the charge for consuming water or electricity service, while connection subsidies are a one-time reduction in connection charges. Consumption subsidies are available only to existing utility customers. Connection
variables (Panel B). Our worker analysis relies on more than 342,000 workers (about 3 million worker-year observations). On average workers are employed 10 months per year. This lower than full-year (12 months) work average can be rationalized by the fact that the sample includes 19% of observations with zero months worked (added to the worker panel database as described earlier). The average monthly real wages are 2,725 in 2010 Reais (about 831 USD at the 2015 exchange rate).<sup>14</sup> Our firm analysis relies on 15 thousand firms (about 119 thousand firm-year observations).<sup>15</sup>

> 12For these firms the panel includes all years of data (even years when the firm is not exporting).

> 13The PIA survey is mandatory for firms with either more than 30 employees or above a revenue cutoff and also for an annual random sample of smaller firms, as described by Alvarez et al. (2018). Estimation based on the PIA firm panel database was conducted in a secure room at the IBGE premises in Rio de Janeiro.

> 14Summary statistics on worker controls in Appendix Table A1 show the workers in the sample average 33 years of age, a quarter are female and only 16% have higher education. Workers were employed in the formal sector about 67% of the time before the GFC.

> 15Appendix Table A1 shows almost 60% of firms are importers, a finding that is not surprising as the sample covers firms that export and the phenomenon of importing-to-export is common. Firms experience on average substantial annual growth in employment prior to the GFC of 11%, but annual growth in total wages is only 2% higher, resulting in moderate growth in average wages that is smaller than inflation rates.

9
: 387-407.

- Hoekman, B. and B. Shepherd. 2019. “Services Trade Policies and Economic Integration: New Evidence for Developing Countries,” EUI Working Paper RSCAS 2019/57.

- Jafari, Y. and D. Tarr, 2017, “Estimates of Ad Valorem Equivalents of Barriers against Foreign Suppliers of Services in Eleven Services Sectors and 103 Countries,” _The World Economy,_ <mark>40(3): 544-73.</mark>

- Kox, H. and A. Lejour, 2005. "Regulatory heterogeneity as obstacle for international services trade," CPB Discussion Paper 49, CPB Netherlands Bureau for Economic Policy Analysis.

- Miroudot, S. and B. Shepherd. 2016. “Trade Costs and Global Value Chains in Services,” in M. Roy and P. Sauvé (eds.), _Research Handbook on Trade in Services._ Edward Elgar.

- <mark>Moïsé, E. and S</mark> . Sorescu. 2013. "Trade Facilitation Indicators: The Potential Impact of Trade Facilitation on Developing Countries' Trade," Trade Policy Papers 144, OECD Publishing, Paris.

- Mustilli, F. and J. Pelkmans, 2013, _Access Barriers to Services Markets Mapping, tracing, understanding and measuring_ , Brussels: CEPS Special Report 77.

- Nordås, H. 2016, “Services Trade Restrictiveness Index (STRI): The Trade Effect of Regulatory Differences”, Trade Policy Papers 189, OECD Publishing, Paris.

- OECD, 2017, _International Regulatory Cooperation and Trade: Understanding the Trade Costs of Regulatory Divergence and the Remedies_ . Paris: OECD.

- Weissinger, R. 2019. “Methods to Determine Needs for Service Standards,” University of Geneva, mimeo.

- WTO. 2019. _World Trade Report 2019: The future of services trade_ . Geneva: WTO.

24
AGE GROUP**||||
|Less than 26|31,778|284,352|9,549|
|From 26 to 35|8,148|180,417|66,278|
|From 36 to 45|22,443|129,941|40,089|
|From 46 to 55|15,395|118,860|1,750|
|More than 55|10,347|58,197|3,185|
|**BY GEOGRAPHIC LOCATION**||||
|Capital|-37,911|240,194|5,394|
|Center region|23,423|114,121|24,558|
|Center gulf region|-1,310|34,349|20,172|
|North central region|14,242|30,074|7,641|
|North pacific region|-1,385|25,571|12,240|
|Central pacific region|29,402|79,003|19,278|
|South pacific region|-4,104|1,648|16,611|
|Peninsula region|5,261|25,126|6,919|
|Border|60,493|221,681|8,038|

**Source: Own calculations using data from ENOE.**

19
|0.040|0.098|0.197|

_Notes_ : Difference-in-difference regressions with robust standard errors. * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. Full sets of control dummies are included for household size and the type of construction material used for the dwelling (permanent, semipermanent or traditional). The dependent variable in columns (7) to (9) is a dummy that equals one for households that report to have experienced blackouts at least several times a month over the last 12 months.

_Source:_ Authors’ analysis using data from the Living Standards Measurement Study (LSMS).

According to the point estimates of the interaction term in columns (7) to (9) of Table 2, the project was associated with a decrease in the share of households reporting regular blackouts of 17.9 to 21.6 percentage points (from the baseline of 94.5 percent in 2013 in treated areas; see Table 1). However, only the coefficient (0.216) in column (8) is statistically significant at the 10-percent significance level (the same holds when standard errors are clustering at the stratum level). One reason for the low (or lack of) significance in the regressions in columns (7) to (9) might be the relatively small number of observations, which is due to the small share of households with access to electricity in our sample (the survey only asked households with electricity in their homes about the frequency of blackouts).

Table 3 presents the results of estimating equation (1) for the two outcome variables capturing whether households relied on electricity as the main source of energy for lighting and cooking or not, 11
reflected in) changes in government policies and institutions. Alm, Martinez-Vazquez and Torgler (2006) use therefore micro-level data for Russia from the World Values Survey and the European Values Survey for the years 1991, 1995, and 1999 to examine how these attitudes have changed during the tumultuous events of the 1990s.

The 1990s were a transforming decade for Russia. This period begins with the dissolution of the Union of Soviet Socialist Republics in December 1991. It spans the presidency of Boris Yeltsin, who was elected President by popular vote in June 1991 and who served until December 1999, when he resigned and Vladimir Putin became acting President. Putin was elected president shortly after with considerable popular support and authority to carry out a backlog of reforms. The 1990s were also a period during which major legislation was enacted<sup>9</sup> . Abstracting these legal changes, many other events also shaped the perceptions of ordinary citizens toward the new state. Notable socio-political events include: the August 1991 coup attempt; the political struggle and final victory of Yeltsin over the communist-dominated parliament; the centrifugal separatist forces in ethnic regions from Chechnya to Tatarstan to Bashkiria; the massive privatization of state assets and the rise of the Russian oligarchs; the questionable alliance of the oligarchs with the Kremlin for Yeltsin‘s reelection; the financial crisis and debt default of August 1998; the beginning of the economic recovery and the consolidation of power in Putin‘s hands the next year. The Russian Federation is characterized by strong ethnic, economic, and fiscal disparities between the regions, including some sorts of secessionist forces (Polishchuck, 1996). In general, the relationship between the Krelim and the regions during the 1990s went from laissez faire in the early Yeltsin years, to open conflict and full defiance in the middle of decade when many of the regions maintained secret bilateral treaties with Moscow, to recentralization and the imposition of more uniform discipline toward the end of the decade, especially after Putin took power. However, the relationship between Moscow and the
elsewhere, but rather as active participants in an increasingly global economic system that could be driven just as much by emerging markets as established ones. While we recognize the institutional constraints that patent owners may face, and the fact that developing countries are lagging behind the developed world in fostering genuinely domestic innovation, we argue that there are ample opportunities for patent owners in developing markets to leverage their IP to promote economic growth. We suggest that the IP ownership divide may be closing, but that substantial differences persist in the extent of IP commercialization in various countries. Emerging markets that are becoming global leaders in IP ownership seem to lag behind in relation to the financial returns they are able to generate from their innovations. While bilateral licensing remains a valuable mechanism to extract value from IP, we posit that developing countries can capitalize on both market- and policy-driven IP monetization mechanisms in order to promote active technology markets. These include patent securitization, patent exchange platforms, public-private technology transfer initiatives, and public support in patent litigation procedures. A combination of institutional support and the adoption of alternative commercialization processes should help developing nations promote stronger technology markets and generate more financial returns from IP in the future.

The paper is structured as follows. In section 2, IMF licensing statistics are reviewed for a small sample of countries in order to increase understanding of the degree of IP commercialization internationally. Then, statistical evidence from the WIPO database on global patent stocks is used to demonstrate the growing ownership of IP in developing nations. The WIPO data suggests that the IP divide, defined as the gap in IP ownership between developed and developing countries, is gradually narrowing for the more technologically sophisticated developing countries. However, a significant divide persists in terms of IP commercialization. While this may be driven by differences in patent quality around the globe, we focus here on the relevance of IP monetization options in addressing this divide. Section 3 briefly outlines the key concepts of patent monetization and presents multiple case studies in order to identify some of the instruments available to commercialize
11 excluding remittances situation, households receiving internal remittances (from Guatemala) have higher mean incomes (expenditures) than do households receiving international remittances (from USA). Since internal migration should generally be less expensive than international migration, this outcome is unexpected. Perhaps the best explanation for this paradoxical outcome is that while migration may have its costs, the economic costs of migration are not the only factor “explaining” the willingness of people to go work in another place. The final finding in Table 3 is quite expected, namely that remittances really increase the level of household expenditure. In the including remittances situation, the average level of expenditures for households receiving internal and international remittances is 43.7 and 46.9 percent higher, respectively, than that for households not receiving remittances. Remittances – from within a country or from abroad – significantly boost the income (expenditure) of those households that are able to produce a migrant.

# 3.  Remittances and Poverty

Now that per capita household expenditures have been predicted in the two situations – excluding and including remittances – for the three groups of households, it is possible to examine the impact of these financial transfers on poverty in Guatemala. This is done in Table 4.

Table 4 is based on the World Bank poverty line for Guatemala in the year 2000 of 4319 quetzels/person/year. This poverty line is defined as the level of per capita expenditures needed to meet the costs of attaining minimum food requirements of 2172 kilocalories/person/day. To this food line the costs of meeting minimum nonfood
that received soil analyses and recommendations during the intervention (arms _T_ 1 _− T_ 4) and examines the take up of the precision drill during sowing (column 1), the two fertilizer packages (columns 2 and 3), attendance at AEW group meetings and the total number of AEW plot visits (columns 5 and 6). Column 7 uses as the dependent variable the sum of the dependent variables in columns 1-3, 5 and 6 while column 8 uses a standardized index of the outcome in column 7. The take up of these items was verified both from farmer reports as well as administrative data and as a result, mis-classification is not an important concern. The penultimate row of Table 5 also reports the mean of the dependent variable among farmers in _T_ 4 (with the corresponding standard error). The control group is not included in these regressions as no intervention was offered to them.

> 28Note that we can recover the overall impact of being in any treatment arm by combining the _β_ -coefficients. Thus, the test that _T_ 1 = 0 is equivalent to _βE_ + _β I_ + _βG_ = 0, _T_ 2 = 0 is equivalent to _βE_ + _βG_ = 0 and _T_ 3 = 0 is equivalent to _βE_ + _βG_ + _βF_ = 0. Finally, _T_ 4 = 0 is equivalent to _βE_ = 0, which is already reported.

16
consequently, the tax system’s burden on firms may also increase compliance. Thirdly, firms that face substantial competition from the informal sector are more likely to report higher levels of tax evasion. This is likely because formal firms in competition with informal firms have a disincentive to comply, as this will lower their competitiveness in the market (such as by lowering their profit margins). Interestingly, this result suggests that there may be a positive externality from registering informal firms if this leads to formal firms being more likely to comply. Notably, there were limited differences in tax evasion rates across other dimensions. As such, any efforts by tax collectors to reduce the rate of tax evasion by most types of firms are likely to raise revenue.

This study has also shown the value of embedding double-list experiments in large-scale surveys. While single-list experiments have grown popular over time, there has also been increasing recognition of some of their shortcomings, particularly regarding the possibility of specific non-sensitive items in a list influencing the likelihood of respondents also counting the sensitive item. Double-list experiments verify if this issue is present and can help illustrate the internal consistency of findings. Our examination of heterogeneity across 27 dimensions with a large sample size is also an important learning exercise as it shows the value of combining machine learning algorithms with indirect solicitation techniques. However, the double-list experiment approach also raises important questions about the best way to report heterogeneity in instances that are not substantial and/or consistent across both list experiments. At a minimum, our study shows how, at a low marginal cost, a double list experiment can provide much more credible insights about the prevalence of a sensitive behavior than a single list experiment and this approach can identify the dimensions of heterogeneity in the prevalence of a sensitive behavior.

Future research on this topic could take several directions. Firstly, additional research could be conducted measuring levels of tax evasion in Indonesia to validate our findings by analyzing tax administrative data with third-party information and/or survey data that directly captures tax evasion and tax
|10|17|9|17|17|15|10|17|16|16|
|Other|11|19|18|20|12|21|17|15|10|15|16|16|12|20|18|20|
||||||Ty|pe of h|ouseho|ld (HH)|||||||||
|HH without|||||||||||||||||
|children|12|14|12|16|11|16|9|14|9|13|13|13|11|14|12|14|
|Retiree HH|9|6|10|10|5|7|8|9|3|4|7|7|3|6|7|8|
|HH with children|13|22|21|21|15|22|15|19|10|17|16|16|12|19|17|19|
|1 child|15|25|23|22|18|23|20|21|8|16|17|17|13|20|18|21|
|2 children<br>3 and more|13|20|19|21|16|20|5|15|14|18|14|14|12|20|15|17|
|children|3|16|14|15|‐15|17|19|13|9|23|17|13|5|12|17|11|
|singleparent|0|8|4|3|‐1|2|8|6|‐5|2|‐2|3|0|4|‐1|7|

Source: Authors’ estimates based on HBS data.

38
last step to obtain the full income distribution is to multiply each of the four income components described above by these adjustment factors, such that total income in the microdata, as well as each of its components, matches aggregates in national accounts.<sup>18</sup> We note that one key implication of the particularly large gap between capital income in the microdata and national account is that, since capital income is predominant at the very top of the distribution, our adjustment to national accounts will substantially increase measurements of concentration of income at the top when compared to measurements that consider only microdata.

> 16 We provide levels of income in national accounts data in Appendix C.

> 17When adjusting microdata to national accounts, we sum distributed capital income and corporate profits in the microdata, and match it to the sum of household property income and corporate profits in the national accounts. We discuss in more details these decisions in Appendix C; the main reason for making the joint adjustment is that, when compared to other countries in the region and worldwide, the share of corporate profits in total income in the National Accounts is very large in Honduras and while property income is often negative. The sum of the two components is more in line with other countries.

> 18Following the DINA guidelines, we also distribute production taxes proportionally to income, so it has no bearing on inequality measures but it does affect income levels.

12
and poverty and income-distribution implications.” _Oxford Development Studies_ 35(3): 273-293.

- Schwarze, Johannes. (2003) “Using panel data on income satisfaction to estimate equivalence scale elasticity.” _Review of Income and Wealth_ 49(3): 359-372.

- Steiger, Darby Miller, Tina Mainieri, and Linda Stinson. (1997). “Subjective assessments of economic well-being: Understanding the minimum income question.” In _annual American Association for Public Opinion Research Conference, Norfolk, Virginia, in the Proceedings of_

25
# **4. Poverty and overall economic growth**

Any poverty measure found in practice can be written as a function of the survey mean relative to the poverty line and the relative distribution of income, as represented by the Lorenz curve.<sup>42</sup> When the poverty line is fixed in real terms, all such poverty measures are strictly decreasing functions of the mean (<sup></sup> _t_<sup>) for any given relative distribution (though the elasticity</sup> can vary greatly, depending on the initial mean and Lorenz curve). A higher growth rate may also entail a shift in distribution for or against the poor. In characterizing the overall poverty impact of growth, we are interested in the total effect of growth on poverty, allowing distribution to change, rather than the partial effect, holding distribution constant.<sup>43</sup> We call this the “growth elasticity of poverty reduction”, or “growth elasticity” for short.

We estimate the growth elasticities at the national level by the regression coefficient of log poverty measure ( ln<sup>_P_</sup> _t_ ) on log mean per capita consumption ( ln<sup></sup> _t_ ) across the available time series, allowing the error term to be autocorrelated and heteroskedastic. Whenever both the dependent and independent variable of such a regression are estimated from the same survey data the possibility arises of bias due to the fact that measurement errors in the survey can be passed onto both variables; when overestimating the mean, for instance, one will tend to underestimate poverty. (The sign of the bias is theoretically ambiguous since measurement error in the independent variable will also induce an attenuation bias in the least squares estimate of the elasticity.) In all our regressions we have also tried an Instrumental Variables (IV) estimator, in which the instruments excluded any variables derived from the same survey as the dependent variable. Our estimates also