rafmacalaba/data-use-mentions-extended huggingface dataset:
opportunities for low carbon energy transition in Vietnam: A policy analysis. _Energy Policy_ , 134: 110818.

- Stone, J. R. N. (1954). Linear Expenditure Systems and demand analysis: an application to the pattern of British demand. _Economic Journal_ , 64: 511- 527.

- Thai, D.M., Falch, M. (2018). Universal service in Vietnam: An institutional approach. _Telecommunications Policy_ , 42: 323–332.

- Trinh, A.N. (2018). Local Insights from the Vietnamese Education System: the impacts of imperialism, colonialism, and neo-liberalism of globalization. _The International Education Journal: Comparative Perspectives_ , 17(3): 67-79.

- Vu, L.H. (2020). Estimation and analysis of food demand patterns in Vietnam. _Economies_ , 8, 11.

- World Bank (2016). Vietnam-Household Registration Study 2015. http://micro data.world bank.org/index.php/catal og/2729

- World Bank (2020). Vibrant Vietnam: Forging the Foundation of a High-Income Economy. World Bank, Hanoi. https://openknowledge.worldbank.org/handle/10986/33831

- Yin H, Zhou H, Zhu K (2015). Long- and short-run elasticities of residential electricity consumption in China: a partial adjustment model with panel data. _Applied Econonomics_ , 48(28): 2587-2599.

25
Country Characteristics

We use two country-level variables. The first is a country’s economic development proxied by _Log GDPPC_ , which is the natural logarithm of GDP per capita in constant 2010 US dollars. The second is the index of government COVID policy stringency ( _Policy stringency_ ), which is a comprehensive measure that takes into account nine response indicators, including school and workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home orders, public publicity campaigns, restrictions on internal activities 11
|ini points)||
|Source: WorldBankestimate|s based on HBS2014.|||

cost of these programs ranges from an additional PLN 7.5 (0.4 percent of GDP) to PLN 19 million (1.1 percent of GDP) relative to the cost of Family 500+ program alone. This estimate takes into account an expected increase in consumption and therefore in indirect taxes (under the assumption that households consume a similar basket of goods).

Table 7. Impact of Family 500+ and Alternative Tax Allowance Reforms

||Market<br>Income|Market income<br>+ contributory<br>pensions|Disposable<br>Income|Consumabl<br>e Income|Final<br>Income|Estimated Fisc|al Cost|
|---|---|---|---|---|---|---|---|
||(1)|(2) = (1) +<br>contributory<br>pensions|(3) = (2) -<br>direct taxes -<br>contributions<br>+ direct<br>transfers|(4) = (3) -<br>indirect<br>taxes|(5) = (4)<br>+ inkind<br>transfers|(million PLN per<br>year)|(percent<br>of GDP)|
|**Gini**||||||||
|202014 Baseline|||0.3428|0.3547|0.2821|||
|1) Family 500+|||0.3133|0.3270|0.2704|22,181|1.3%|
|2) PIT allowance at PLN 5000|05265|03759|0.3397|0.3517|0.2799|8,873|0.5%|
at all, between developed and developing countries? To the extent we know, the

> 4 The data for Table 1 come from a variety of sources.  The main sources are households’ surveys, the LSMS (Living Standard Measurement Surveys). Here individual households responses on questions of usage of financial services are averaged for each country.  Second main sources are the surveys conducted by FINMARK and genesis in a number of Southern African countries.  Again, these are household surveys, but more specifically aimed at usage of financial services. The source for the EU -countries is Pesaresi and Pilley, 2003.  For the US, the source is the Survey of Consumer Finance, 2004. For Brazil, Colombia, India and Mexico the recent estimates are from Kumar et al. 2004, Basu et al. 2004, and Solo et al. 2004.  The earlier India numbers are from the regular Indian household survey. The Kenya, Tanzania and Uganda estimates are from Peachey and Roe, 2004.  Questions on financial services usage do vary across the households survey, numbers are thus not necessarily comparable and some of the numbers are only rough estimates.

9
|-0.275+++<br>(0.090)|-0.241+++<br>(0.083)|-0.237+++<br>(0.083)|-0.091+++<br>(0.031)|
|Log per capita consumption||||0.152<br>(0.505)||
|Local Industrial Conditions by Incumbent Firms:||||||
|Labor market strength for district-industry|||0.161<br>(0.102)|0.164<br>(0.102)|0.026<br>(0.041)|
|Inputs / supplier strength for district-industry|||0.485+++<br>(0.098)|0.485+++<br>(0.098)|0.154+++<br>(0.043)|
|Outputs / customer strength for district-industry|||0.388+++<br>(0.140)|0.387+++<br>(0.140)|0.167+++<br>(0.057)|
|Chinitz small suppliers metric for district-industry|||0.279<br>(0.213)|0.279<br>(0.212)|0.337+++<br>(0.129)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|4843|4843|4843|4843|4843|
|Adjusted R-squared|0.128|0.166|0.218|0.218|0.279|

Notes: Estimations quantify the relationship between district-industry employment in new establishments and local district conditions. District-level traits are taken from the 2001 Census. Industrial
implies that estimates of the population of informal businesses and their activity will be missing or incomplete; and sampling frames for developing high-quality, representative survey data will be unavailable. In turn, descriptions of the characteristics of many economies will be lacking information on a sector that is a ubiquitous source of jobs and business activity.

This paper lays out a methodology for conducting an enumeration exercise that generates a representative sample of informal businesses for a subsequent survey; it does so by utilizing an established geographic sampling method, Adaptive Cluster Sampling (ACS). ACS assumes that informal business activity is relatively clustered in certain areas and, in turn, it takes advantage of the information from the discovery of some informal businesses to alter the probability of selection of primary sampling units (PSUs) to capture concentrations of additional informal businesses more efficiently. ACS accounts for this altered probability of PSU selection and is able to provide estimates of the population totals of informal businesses, allowing any subsequent survey of those businesses to be considered representative. In the process, ACS frequently allows for reductions in fieldwork effort without an increase (and often a decrease) in the variance of these population estimates, if they were done repeatedly—a point which is later illustrated in this paper through simulation. The methodology is designed to be implemented in diverse situations, across different countries, while maintaining comparability.

Informal businesses may operate completely outside all or some legal channels (without a tax or business registration or license), or they may include businesses that operate informally by non-compliant behavior, such as employing workers ‘off-the-books’ (Ulyssea, 2018). Operationalizing data collection first requires a clear and functional definition of the underlying population of interest. A useful and basic typology of informality is the following: informality includes partial or full non-compliance with registration and licensing (legal informality), on employment (labor informality), or tax non- or under-payment (fiscal informality) (World Bank, 2020). This paper focuses on businesses’ activities that are _legally_ informal: that is
There are no direct data available for the efficiency of public investment in infrastructure in DRC; using the average value for the 30 Sub-Saharan African countries (excluding South Africa) in the sample compiled by Dabla-Norris et al. (2012, Table 1), the efficiency parameter for public investment,  , is set at 0.37.<sup>16</sup> The parameter  1 is set at the low value of 0.05 initially, which implies that  0, which is solved for residually, is equal to 0.33. The rate of depreciation of public capital, <sup>P</sup> , is set at 0.035, a fairly standard choice.

External public debt as a share of GDP, D<sup>G</sup> /Y, is set equal to 27.1 percent, which is equal to the ratio of general government gross debt to GDP in 2010, as estimated in the IMF’s WEO database. Thus, all public debt is assumed to result from foreign borrowing. From the estimates compiled by Boyce and Ndikumana (2012, Table 1), the stock of private capital flight for DRC in the same year represented a staggering 258.4 percent of GDP.<sup>17</sup> Thus, the economy’s net stock of external debt, as a share of GDP, can be calibrated at 27.1 - 258.4 = 231.3 percent. By this metric, in the initial equilibrium the country is a net creditor to the rest of the world. However, as noted earlier, due to market imperfections only the public debt matters in the determination of the risk premium.

The world risk-free interest rate (in foreign-currency terms), r<sup>WR</sup> , is set initially at 0.017, which corresponds to the difference between recent averages on nominal yields on U.S. treasury 30-year bonds and an average rate of U
the stock (rates) of children enrolled in secondary school in a given year, while our previous measure represents the flow of new students enrolling in secondary schools. Second, the census-based variable includes children in the village who are enrolled in primary schools that are not eligible for the API program, which converts the analysis of the program at scale into an intent-to-treat analysis. Another educational outcome from the population census that we use is the rate of child literacy for children between eight and fourteen years old, which represents an available measure of children’s achievement. This variable is available

> 20The match between the universe of schools and the localities of the population Census is one to one, as each village has at most only one primary school. The coverage of the census data is not universal, and we were not able to match nearly one-third of the 2,063 schools in Chiapas in 2019. For both educational outcomes in the census data we cannot reject the hypothesis that the probability of missing observations is balanced with respect to the program assignment ( _p_ -values for secondary school enrollment and child literacy are 0.728 and 0.430, respectively). For further details on the census sampling design, please refer to: `https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/ productos/nueva_estruc/702825197629.pdf` , accessed on August, 2022.

35
Both the ineffectiveness of low-level public sector health clinics and the element of individual choice are highlighted in the phenomenon of "bypassing." People often do not go to the closest public facility but "bypass" it in favor of either more costly private facilities or higher level public facilities. There are few empirical studies of bypassing as this requires detailed information on both the health seeking behavior by individuals as well as information on all potential sources of supply. A recent study in Sri Lanka uses detailed surveys of health care supply and demand to document large amounts of blypassing (Sarasinghe and Akin, 1994). Table 2 shows that of all illness episodes only 29 percent are treated at the closest facility (which are predominantly Ayurvedic and "minor public western"), as 31 percent "self-treat" with no medical consultation and, of those that seek some treatment, 58 percent do not go to the nearest facility.'" Of the non-Ayurvedic sources, the "minor public western" facilities, the type that PHC would promote, are bypassed the most. Most individuals who bypass either "minor" or "major" public facilities do so to visit a "private western" facility.

Table 2: Summary of bypassing results of Sri Lankan study

|1||2||3||
|---|---|---|---|---|---|
|Treatment chosen by ill||Percent of times each type|of|For each type of facility i|n column|
|Self-care|31%|facility is bypassed||2, the main type of facflI|tybat the|
|Closest facility|29%|||bypassing individuals go|to|
|||Ayurvedic|40%|Private western|41%|
|Not cls f4|0%|Minor public western|21%|Private western|_50%_|
|Not closest facility||Private westein|10%|Major public western
53, and 54-65) and five education groups based on years of education (1-5, 6-8, 9-11, 12-15, and more than 16 years). Using sample weights, we generate the mean of PPP-adjusted 2005 dollarvalue monthly earnings for each cell. These cells are identified by age, education, country, and year.

To link the micro analysis with the macro analysis presented earlier, Figure 4 compares the mean wages calculated from the household surveys and the log GDP per capita from the 20092013 period. The GDP per capita explains about half of the variation in wages across countries, which suggests that GDP per capita convergence only tells part of the story about labor market integration. Note that Peru, Bolivia, and Paraguay have higher-than-predicted mean wages (given their GDP per capita) while the poorer Central American countries (Nicaragua, Honduras, and El Salvador) have lower-than-predicted wage levels.

# **C. Empirical Results**

To compare wages more formally, the mean wages for each demographic cell from country “A” are matched with the corresponding mean wage from every other Latin American country for each year. We then take the absolute value of the difference for each pair of countries. For illustrative purposes, we present the mean (absolute value of) wage differentials by cell in Figure 5. Figure 5 reveals the first main result from this study: the wage differentials across demographic groups are not constant. They are, however, consistent with, among others, migration theories in the sense that the differentials across countries are lowest for the middle education groups. The middle education groups, of course, are those most likely to migrate. Note that the oldest workers also tend to have the highest differentials. The older workers are least likely to migrate. We illustrate these results more formally in the empirical analysis that follows.

14 | P a g e
who have migrated, dramatically reducing the effective sample size. For example, McKenzie et al. (2010) match migrants from Tonga who applied for a migration lottery program in New Zealand to Tongans in a nationally representative labor force survey. They find that their sample drops from over 4,000 observations to just 354 when restricting to individuals with propensity scores between 0.05 and 0.95 – reflecting that most individuals in the labor force survey are too dissimilar to be used as controls for the migrants.

The result is that in many cases we do not believe matching (or regression) will yield reliable and precise point estimates of the impacts of migration. How much this matters depends a bit on the question of interest. In some cases, the impacts of migration may be so large that the bias from selection is second-order (McKenzie, 2020). For example, McKenzie et al. (2010) use a randomized lottery to estimate the income gain from migrating to be NZ$274 per week, a 263% increase on the control mean. Using regression or matching overstates this gain, with estimates of around NZ$330-350 per week, which are 20-28% higher than the experimental estimate. But in both cases one would conclude that migrating resulted in a massive gain in income, and this bias from matching is an order of magnitude smaller than the treatment effect. Bounding methods can also be used to ask how sensitive 15
<br>**consumption**<br>**aggregates**|**Difference**<br>**(1)- (2)**|
|---|---|---|---|---|
|||**(1)**|**(2)**|**(3)**|
|Bangladesh|2010|19.44|18.52|0.92|
|_- Rural_|_2010_|_23.86_|_22.74_|_1.12_|
|_- Urban_|_2010_|_7.01_|_6.67_|_0.34_|
|Bhutan|2012|3.49|2.17|1.32|
|India<sup>(1)</sup>|2011/12|19.61|21.56|-1.95|
|_- Rural_|_2011/12_|_24.20_|_24.83_|_-0.63_|
|_- Urban_|_2011/12_|_8.13_|_13.38_|_-5.25_|
|Maldives|2009|6.10|7.25|-1.14|
|Nepal|2010|14.51|14.89|-0.38|
|Pakistan|2011/12|9.24|7.93|1.31|
|Sri Lanka|2012/13|2.06|1.92|0.14|
|South Asia||18.31|19.6|-1.29|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). India’s consumption aggregates reported are based on the Uniform Recall Period (URP).

The standardization of the consumption aggregates would reduce the international poverty rate in South Asia by 1.3 percentage points or almost 18.5 million extreme
large number of countries is enrollment in preschool. This section therefore begins with an analysis of preschool enrollment rates in Latin America.

Table 1 presents statistics on the gross preprimary enrollment rate for individual countries in Latin America and the Caribbean, as well as averages for the region. Data are from databases of the World Bank (World Bank 2005b); they include 144 countries, and refer to the year 2000. For each country or region, the table presents three figures. The first column gives the gross primary enrollment rate. The second and third columns are based on regressions of gross preprimary enrollment on a polynomial in per capita GDP, including GDP level, its square, cube, and quartic, as well as a dummy variable for a given country or region. (In the second column, each country observation is weighted by its population, while the third column provides the results from unweighted regressions.) The table reports the coefficient on a country or region dummy, and notes when the coefficient is significant. These regressions therefore benchmark performance in a country or region by comparing it with other countries of similar income levels.

The results from these regressions are informative. Taken as a whole, Latin America and the Caribbean does not appear to have a deficit in preschool enrollment: The (weighted) average of the gross enrollment rate across countries in the region is 61.1 percent. This is above what would be expected for the income level of the region—the coefficient on the dummy for Latin America and the Caribbean is positive, although it is not significant. However, there is a great deal of variation across countries. A handful of countries are “over-performers” in terms of preprimary enrollment rates, including Bolivia, Costa Rica, Ecuador, Guatemala, Jamaica, and Peru. By contrast, a number of other countries are “under-performers”, including Argentina, the Dominican Republic, Honduras, Panama, and Venezuela. Put differently, preprimary enrollment is essentially the same in Argentina and Peru, although per
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

- Giannakis, D., Jamasb, T., and Pollitt, M. (2003). “Benchmarking and Incentive Regulation of Quality of Service: An Application to the UK Electricity Distribution Utilities”, CWPE/DAE Working Paper, Department of Applied Economics, University of Cambridge (forthcoming).

- Harris, C. (2003) “Private Participation in Deve loping Countries: Trends, Impacts, and Policy Lessons”, World Bank Working Paper No. 5, The World Bank, Washington D.C.

- IADB (2001). “Competitiveness: The Business of Growth, Economic and Social Progress in Latin America”, Research Department, Inter-American Development Bank (IADB), Washington, D.C.

IEA (2003a). “Key World Energy Statistics”, International Energy Agency (IEA), Paris.

IEA (2003b). “Electricity Information 2003”, International Energy Agency (IEA), Paris.

- IAEA-IEA (2002). “Indicators for Sustainable Energy Development”, paper presented at Commission on Sustainable Development, 9th Session (UNCSD-9), New York, 16-27 April 2001, International Atomic Energy Agency (IAEA), Vienna and International Energy Agency (AEA), Paris.

Weblink: http://www.iea.org/envissu/files/csd-9.pdf

- Jamasb, T., Mota, R., Newbery, D., and Pollitt, M. (2004). “Electricity Sector Reform in Developing Countries: A Survey of Empirical Evidence on Determinants and Performance”, Cambridge Working Papers in Economics, Department of Applied Economics, University of Cambridge (forthcoming).

Jamasb, T, Pollitt, M. (2003). “International Benchmarking and Yardstick Regulation: An Application to European Electricity Utilities”, Energy Policy 31(15).

32
First, we constructed a panel of nineteen European countries and used data for period 20002007 (basic data sample――S1‖).<sup>27</sup> Out of all the countries in the panel, fourteen are old member countries, one is Norway, which we classify as an old member country for purposes of this paper, and remaining four countries are the NMS. The source of the data is mainly the OECD and partly also Eurostat, World Bank and Heritage Foundation. However, as this sample covers only four NMS (the Czech Republic, Slovakia, Hungary and Poland) we expanded our analysis to other NMS as well in the second, extended data sample (―S2‖). The panel consists of twenty-eight countries, twelve of which are the NMS.<sup>28</sup> Yet, the availability of relevant data is a serious obstacle. Several OECD variables had to be replaced by less accurate indicators provided by the Eurostat. Moreover, the necessary set of indicators is available for a shorter time period of 2006-2007 only. The third sample we use in our analysis (―S3‖) consists of twenty-six countries, ten of which are the NMS.<sup>29</sup> The time period concerned is years 2003 and 2007. The source of the data is mainly the OECD and partly also Eurostat, World Bank and Heritage Foundation, similarly to the first sample. Furthermore, we extended OECD data for information from IZA database, which includes all the OECD measures of labor market institutions for the non-member countries of Central and Eastern Europe.<sup>30</sup> Given the limited amount of data available, the second and the third samples are used for robustness checks mainly.

We examine the impact of institutional factors on five indicators of shadow production and employment: shadow economy as percentage share on overall official GDP ( _SHEC_ ), share of labor force in unregulated self- and wage-employment measured by share of labor force not contributing to the
specific spillover variable is confirmed.

The second method to address the zero foreign export flows, which is used in the remainder of the paper, is to conserve the full sample and to decompose foreign export activities into the presence of foreign exporters for a given product-country pair, as measured by a dummy, and the value of their exports. Note that this decomposition of foreign exports into the presence of foreign exporters and the value of foreign exports is a way to describe the shape of export spillovers: are spillovers log-linear with respect to the scale of foreign export activities, or is there a discontinuity in the impact of foreign exporters that is linked to their sole presence? The results show that, on average, both margins of spillovers have a positive impact on domestic starts (column 4). This specification does not affect our results for the other dimensions of foreign export activities.

Finally, the spatial dimension of foreign export spillovers has been overlooked so far. Some Chinese provinces might be very large, and the interpretation of the results obtained in terms of spillovers implies some geographic proximity. A first answer to this issue is that although the surface area of some provinces (especially those in the western part of China) is rather large, the economic activity is very concentrated. The data for 2000 indicate that roughly one-third of industrial production is generated in the capital cities of these provinces.

23