rafmacalaba/data-use-mentions-extended huggingface dataset:
substitute for childcare services, especially in households with a female head (Tienda and Glass, 1985; King, 2011; Talamás, 2019). However, as stated before, childcare dependency rates dropped, and elderly dependency rates rose since 2007 (Table 1). Although not statistically significant, the coefficient of elderly dependency is negative in 2017.

The literature has shown that Mexican women traditionally bear informal eldercare provisions (Naranjo and Gameren, 2015). As the population pyramid shifts and the share of older people increases, traditional gender roles could cause more women to stop working and take care of older family members (Johnson and Lo Sasso, 2006; Ettner, 1995). The negative effect of having younger children on women’s employability is counteracted by the expansion of childcare facilities, and probably by having older adults who can substitute for childcare, but this might not compensate for rising elderly dependency in the long run.

Government policies—specifically the provision of childcare facilities—can offset the effect of having children. Although not significant in 2007, a one-unit increase in the number of childcare facilities per 1,000 people in the municipality is associated with a 13.8 percentage point rise in a woman’s propensity to be employed 10 years later. The positive effect is not exclusively for mothers; having more childcare facilities increases _all_ women’s likelihood to be employed. It is possible that other women within the household, or in mothers’ close network, are also entering the labor market as they are less constrained to provide childcare.

Three reasons could explain the lack of a significant effect in 2007: (i) we are using the 2008 childcare directory, with much more childcare facilities, instead of the 2007 directory, (ii) we are evaluating CFWM’s impact soon after its implementation, leaving no opportunity for positive 28
0.116)|||||||(0.085)|(0.065)|
|Obs|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|

Note: All regressions include importer-year and exporter-year fixed effects. Columns (3) to (10) include also country-pair fixed effects. In columns (3) and (4), the _TA_ dummy equals one if the two countries in the pair belong to the same TA (with or without provisions on government procurement). Data are for the years 1995, 1999, 2003, 2007, 2011 and 2015. Coefficients in bold are statistically different between the ‘Gov.’ and ‘Priv.’ regressions. Robust standard errors clustered at the country-pair level are in parentheses. Significant at: *10%, **5%, ***1% level.

The baseline results rely on the use of dummy variables to identify the presence of provisions on government procurement in TAs. The DTA database of Mattoo et al. (2020) however provides also detailed information on the inclusion of specific provisions in each TA. We thus exploit this information to gain a better understanding of the type of measures that can drive the average trade effect of covering government procurement in a TA. The database includes one hundred questions about the treatment of government procurement in the legal text of TAs. We consider the ‘horizontal’ depth of the government procurement chapters (if any) and exclude information about the treatment of certain provisions that are difficult to classify as moves towards opening up the market.<sup>25</sup> After this cleaning of the data, we end up with 39 distinct provisions on government procurement.

> 25Specifically, we drop questions that measure ‘vertical’ depth – i.e., those about the content of phasing in provisions for developing
of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

61
, Vol. 7, Number 4, November, 543-562.

- Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2009). "Estimating trade restrictiveness indices," _Economic Journal_ , Vol. 119, 172--199.

- Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga. (2008), "Import Demand Elasticities and Trade Distortions", _Review of Economics and Statistics_ , 90 (4), 666—682.

- Kehoe, Timothy J. (2003), "An evaluation of the performance of applied general equilibrium models of the impact of NAFTA," Staff Report 320, Federal Reserve Bank of Minneapolis.

- Konan, Denise Eby and Keith E. Maskus ( 2006), "Quantifying the impact of services liberalization in a developing country," _Journal of Development Economics_ , Vol. 81(1), pages 142-162.

- Krueger, Anne O. (1974), “The Political Economy of the Rent-Seeking Society,” _American Economic Review_ , Vol. 3, 291-303.

- Krugman, Paul (1979), “Increasing Returns Monopolistic Competition and International Trade,” _Journal of International Economics_ , 9(4), 469–479.

- Krugman, Paul. (1980), “Scale Economies, Product Differentiation, and the Pattern of Trade,” _The American Economic Review_ , 70(5), 950–959.

- Levy, Santiago and Sweder van Wijnbergen (1995), "Transition Problems in Economic Reform: Agriculture in the North American Free Trade Agreement," _American Economic Review_ , Vol. 85(4), September, 738-54.

- Limão, Nuno (2016), “Preferential Trade Agreements,” in _Handbook of Commercial Policy,_ Vol. 1B, Kyle Bagwell and Robert Staiger (eds.), Chapter 6, 279-360, Amsterdam: North Holland.

44
# **B. Generation and transmission planning index for Vietnam and comparators, 2015**

||**Colombia**|**Peru**|**Philippines**|**Vietnam**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Planning and Procurement**|95%|77%|59%|59%|70%|
|**Generation Planning**|86%|43%|71%|71%|56%|
|Countryhas ageneration masterplan|||||94%|
|Countryhas an overall energy plan|||||65%|
|Competent entityis responsible forproducingthe|||||88%|
|Inter-governmental committee oversees theplanning|||||29%|
|Powergeneration systemplan is mandatory|||||19%|
|Plan leads to timelyinitiation ofprocurement|||||38%|
|Planning process is transparent andparticipatory|||||59%|
|**Transmission Planning**|100%|75%|50%|100%|72%|
|Competent entityis responsible forproducingthe|||||100%|
|Explicitlylinked topowergenerationplans|||||88%|
|Plan is mandatory|||||29%|
|Planning process is transparent andparticipatory|||||71%|

# **C. Generation and transmission procurement index for Vietnam and comparators, 2015**

||**Colombia**|**Peru**|**Philippines**|**Vietnam**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Procurement of
||
|(value added weights, former<br>national<br>accounts<br>concept)|1.6818|1.5573|1.6184|---|
|Weighted average PPP for sampl|e||||
|(value added weights, US<br>|||||
|census concept)|---|---|---|1.7068|
|Exchange<br>Rates|1.5375|1.5375|1.5375|1.5375|

> Source: pesos/cruzeiro PPPs derived from table 4.7. Includes adjustments for indirect taxes and subsidies for malt and malt beverages, tobacco and tobacco products, and petroleum refining and products. Inferential peso/cruzeiro PPPs derived from table 3.13 (chapter III).
Figure 1:Literacy Inequality and GDP <!-- Start of picture text --> 2010 Ratio of Female to Male Literacy 2010 Ratio of Female to Male Literacy<br>Ratio of Female to Male Literacy Regression Coefficient: 6.74<br>Median=0.99 Standard Error:0.81<br>.4 .6 .8 1 1.2 .4 .6 .8 1 1.2<br>Ratio of Female to Male Literacy Ratio of Female to Male Literacy<br>.6 12<br>10<br>.4<br>8<br>.2<br>Fraction of Countries<br>6<br>Log of 2009 Per-Capita GDP<br>0 4<br><!-- End of picture text --> Literacy rates taken from the 2010 World Economic Forum Gender Equality Report
# **Annex 1 – Variables’ definitions**

|**Variable**|**Description**|
|---|---|
|**Log reservation wage**|The logarithm of the wage self-reported as the level below which the<br>respondent would not accept a job offer, per assumed work hour of a<br>standard working week. The variable is observed only for unemployed<br>persons.|
|**Log market wage**|The logarithm of the wage the respondent received at the time of the<br>interview, per actual work hour. The variable is observed only for<br>unemployed persons.|
|**Female**|1 if persons is women, 0 otherwise|
|**Age**|Age expressed in years|
|**Married**|1 if persons is married, 0 otherwise|
|**Number of children**|Number of children living in the household|
|**Education**|Three dummy variables for primary, secondary and tertiary education.<br>Primary is the reference category.|
|**Recipient of unemployment**<br>**benefits**|1 if the person received state unemployment compensation in the 4 weeks<br>preceding the interview, 0 otherwise|
|**Unemployment spell duration**|The period, expressed in years, that elapsed since the last time the person<br>was employed.|
|**Log of household’s welfare**|Calculated variable, based on coefficients obtained from SILC. Note that<br>LFS does not contain data on income except for wages. The following<br>equation was used: Log of household’s welfare = 0.151*Log of the wage<br>per hour + 0.237*Average education level in the household +<br>0.022*Average age in
# consumption level is equal to his current disposable income.<sup>8</sup>

In each period a worker devotes a fixed amount oftime to one of the following two activities: working in the formal sector or working in the informal sector of the economy. Our informal-sector data in the empirical section are taken from the self employed and we will therefore call a person working in the informal sector a self-employed worker. In each period, a worker, regardless of his current employment status, receives an idiosyncratic shock determining the relative attractiveness of employment versus self-employment ("taste-shock", change in expected payoff to selfemployment). After observing the shock realization, an employed worker makes a quit/stay decision and a self-employed worker makes a search/no-search decision. Whereas an employed worker automatically becomes self-employed when deciding to quit a job, a self-employed worker who decides to search for formal-sector employment receives ajob offer only with probability less than one.

The Appendix Al discusses the Bellman equation associated with the worker's decision problem and analyzes the resulting optimal decision rule. The optimal decision rule gives rise to a quit-ratefunction, q. = q(w,,w,p;T,b),where q, standsforthequitrateexperiencedbyfirm i, _w1_ for the (growth-adjusted) wage paid by finn i, w the average (growth-adjusted) wage, p for the probability of finding (formal sector) employment when not employed, X for the tax rate on formalsector labor income, and _b_ for the average (growth-adjusted) pecuniary benefits from self employment.

Let _q(w,p;t,b) i q(w,w,p ;_ T,b) _be the economy-wide quit-rate fimction when all 8ln a sense, this assumption renders the model classical rather than neoclassical. It is mainly made for tractability reasons since it trivially deterrnines the wealth distribution of workers (no wealth). Without this assumption the wealth distribution is in general non-trivial and has to be computed as part of the equilibrium, except
as product categories get narrower. Second, it assumes that market share changes are independent of demand growth. Normally, one would expect that when the rate of growth of demand accelerates, there would be spillovers to imports that will increase the import shares. This relationship was tested using the both the detailed 3-digit subsector information using imports and exports from developing countries and aggregate global imports for the selected 13 countries. The relationship between the market share changes and demand changes was not significant across industries and countries.<sup>6</sup>

> 5 There is a significant appreciation of the US dollar against the currencies of most the other countries during the late 1990s and depreciation during the 2000s. This appreciation underestimates the domestic production and demand growth in US dollars and overestimates the share of imports, which are denominated in US dollars. Opposite takes place during US dollar depreciations. Thus, nominal US dollar measurement would underestimate real growth during the 1990s and overestimate it during the 2000s.

> 6 The disaggregated estimation was done at the 3-digit ISIC level correlating the 22 sub-sector import growth rates and corresponding import growth rates due to market share changes, with country dummies.  At the aggregate level the relationship

5
for cigarettes are used to incorporate heterogenous behavioral responses by different income groups of the population.

The estimates of price-elasticities suggest that -when faced with higher cigarette prices- poorer households in Georgia tend to reduce their consumption more intensely than higher-income peers. The magnitudes (under the base-scenario) of those elasticities are higher than similar exercises in other country settings. The ECBA is simulated on household expenditure data from the Household Income and Expenditure Survey (HIES, formerly the Integrated Household Survey, IHS). The findings suggest that the net distributional effect of increasing taxes on cigarettes in Georgia is not regressive, but instead highly progressive. Increasing prices of tobacco dissuades smoking and allows households to free up resources for other consumption goods. Incorporating the indirect benefits of reducing smoking—reductions in tobaccorelated medical expenses and additional earning from avoiding premature deaths—further offsets the negative price shock from the tax increase on household budgets. In the long-run, the net effects are positive across the population, with poorer households receiving the highest benefits, relative to their incomes.

The next section provides a background on the consumption patterns, control measures and taxation policies of tobacco products in Georgia. Section III. reviews the previous literature on the health and economic consequences of tobacco consumption and surveys existing research in Georgia. The fourth section outlines the components and theoretical assumptions of the ECBA methodology. Section V. presents an overview of the data sources and basic descriptive statistics. Simulation results for each component of the model, as well as the net distributional income effects are reported in Section VI. The results are then used to simulate the potential changes in poverty, and the number of lives saved from the tax increase. The final section discusses the likely implications of these results for the policy and research agendas in Georgia.

2
World Bank Enterprise Survey (World Bank, 2013), less than 3 percent of Mongolian firms reported labor-market legislation as a constraint on labor demand. Yet, a quarter of sampled firms stated that the main obstacle to hiring was the inadequacy of skills in the workforce and mismatching between prospective employees’ skills and market needs. If such mismatching exists, it is important to assess the effectiveness of training programs tailored toward providing the vocational skills that are in demand in the market. One potential barrier for such investments could be the existence of compressed salary scales—typical under centrally planned economies—because firms might be unable to reward applicants who have additional skills. The evidence is, however, not clear in this regard. In 2012, large and increasing labor-market returns were observed for each additional year of schooling.<sup>3</sup> Nonetheless, firm-level data showed that only 22.6 percent of Mongolian enterprises had a performance-based wage system and only 7.9 percent had skill-based wage systems (Research Institute of Labor and Social Protection, 2017). Furthermore, although there is no legislation that prevents firms from adopting new screening technologies in the labor markets, the awareness and utilization rate of government employment services is very low (less than 20% of idle youth in Ulaanbaatar indicated they were aware of employment websites, electronic job boards, and other types of public support; see Shatz et al., 2015).

# **3. The Mongolian Vocational-Training Program (MVTP)**

The MVTP was introduced in 2003 to counteract high levels of unemployment and idleness. Its primary goal was to help those who were poor, unemployed, or vulnerable to unemployment find jobs through a combination of vocational-skills training and internships in business firms, and it remains the oldest and most extensive labor-market policy in effect

> 3 The annual rate of return for an additional year of schooling ranges from 7 to 10 percent (Batchuluun and Dalkhjav, 2019; Shatz et al. 2015).

8
sup>_∗∗∗_</sup>||
|Obs.|107,523|130,721|50,303|44,720|
|e(N-g)|6,274|6,444|6,052|5,846|
|_R_<sup>2</sup>|.047|.06|.068|.06|
|Test LM-UM (p-value)|.021||||

Note: Standard errors are robust and clustered at the school level. Other variables included in the regressions are the student’s number of books at home, gender, repeat, class size and age. Source: ONE 2000. Argentina But the tendency for a child at the bottom to perform better diminishes when the social distance is too large.

Finally, for robustness I ran the previous three regressions using alternative definitions of the dependant variable, the main peer variable, and type of school attended, to see the robustness of results found. The results are summarized in table 8 with regressions results in the appendix (tables 18 to 21).

One could argue that the rules of randomization across classes are more relevant in the context of public (state) schools than in private ones. When registering a new student into a school, principals are not meant to request information other than date of birth and formal education history of the child. Private institutions are not subject to such regulation. Indeed, some private establishments request the candidates to take tests, to provide information of parents education and occupation, and to include references. Additional information of candidate’s ability and social background can be use to assign pupils into classes. I therefore run the same analysis exclusively for public schools. All the previous results hold.

A second set of robustness analysis involves using alternative measures 24
populations to avoid biasing survey estimates, specifically underrepresenting the poorest and most vulnerable households and individuals. While reweighting techniques function to counteract these biases and improve representativeness in important ways, they cannot be an all-around remedy for systematic under-coverage and nonresponse. One option is to provide households and individuals who would normally not have access to one with a mobile phone, but that requires considering not only cost implications but also how having a phone may alter respondents behavior in areas the survey is actually aiming to investigate, such as access to market information or extension services. And if phones are given to households participating in the surveys, protocols will have to be devised on how many phones per household to hand out and who receives them, as informational content can be enhanced, and biases reduced or introduced, with different decisions in this domain.

Given the remote nature of phone interviewing, respondent burden and attrition deserve particular attention. Survey designers should make use of the tools at their disposal, especially questionnaire design, survey timing, contact protocols, respondent selection, and incentives, to minimize burden, curb nonresponse, and avoid attrition, within the limits dictated by the survey objectives. As for the frequency, the nature of data collected and the survey budget are key factors in determining the desired frequency. Arthi et al. (2018) and Gaddis et al. (2021) use weekly phone calls to collect agricultural labor data. Kilic et al. (2021) even called twice a week for extended harvest crop production data. In contrast, the LSMSsupported HFPS on COVID-19 collected monthly data, and the Georgia Survey of Agricultural Holdings, which converted to phone-based implementation in light of COVID-19 restrictions on in-person interviewing, is a quarterly survey. In practice, respondent burden and fatigue need to be considered, which very high interview frequencies could conceivably increase, risking higher attrition rates (see discussion in Section 3.1). The extent to which a sequence of short but more frequent phone interviews are less burdensome on respondents than a longer in-person sitting
region (this is the share of total regional income in total national income): it ranges from near zero in Lakshadweep in India<sup>39</sup> to 0.83 in England (U.K.) Using these household income data, we compute income Gini coefficients for each region, measuring within-region inter-personal income inequality. Economic regions in Armenia and Slovak republic all have low levels of inequality and the highest Gini coefficient values are in Brazil, Colombia, and some regions of Nigeria, Senegal, and South Africa.

# **_Natural Resources_**

The relative abundance of natural resources across regions is another important variable in the model. Ideally, we would want to know the value of all territorially concentrated tradeable resources in each region. Any number of primary commodities could be important for the local or national economy (coffee in Burundi and Colombia, diamonds in Sierra Leone and South Africa, natural gas in Indonesia and Russia). The ideal measure is thus the share of total national resources for each region, but such a measure can only be computed for a few countries and a small number of agricultural commodities, minerals, and precious stones due to the paucity of data on the location of different commodities and the lack of region-level breakdowns of the total dollar-value of production or trade in these commodities. Thus our analysis is inevitably limited. We collected data on oil and natural gas and identified with a binary variable all the regions that have substantial amounts of these resources (exploited or unexploited reserves are coded; see supplement for coding details and geological maps for each country).

# **_Regional Ethnic Difference_**

To measure regional ethno-cultural differences from the rest of the country, we identify all groups larger than 1% of the country’s population and collect data on the size of as many of

> 39 Data for India are for rural areas.  Urban and rural income data are reported in separate surveys. We selected the rural data as more directly relevant to the question at hand.