rafmacalaba/data-use-mentions-extended huggingface dataset:
saving for old age, such as livestock, gold, and stuffing money under the mattress. It could also be indicative of less liquid means of saving for old age, like home ownership.

Figure 2 presents the gender distribution of saving for old age around the world and by region. The global gender gap in terms of saving for old age is in the magnitude of 2.5 percentage points (10.5%), with 26% of males saving for old age, versus 23.5% of females, globally. Notably, this is lower than the 9 percentage point gap in account ownership around the world. The gap is largest in South Asia, and smallest in EAP, which also has the highest absolute savings rates for old age.

Figure 3 then presents the educational gaps in terms of saving for old age. The global education gap is wider between tertiary and secondary education, compared to the respective gap between secondary and primary education (or less). Specifically, 37.9% of individuals with tertiary education save for retirement, compared to 27% of individuals with secondary education and 22.2% of individuals with primary education or less. The savings-for-old-age gap seems to be much wider between the tertiary and secondary education groups, compared to the gap between secondary and primary education or less. The pattern is remarkably strong in LAC, in which 25.3% of individuals with tertiary education save for old age, compared to 11.9% and 8.6% of individuals with secondary and primary education or less, respectively. The exception in this pattern is EAP.

Figure 4 presents the age distribution of saving for old age around the world. The distinction is between six groups, namely those aged 18-25, 26-35, 36-45, 46-55, 56-65 and 65+, respectively. The global age distribution of saving for old age in the 2014 Global Findex data suggests that 10.1% of individuals aged 18-25 save for old age. The figure for those aged 26-35
The relationship between sector and type of economic activity among household members, and household welfare in Uganda has been found in other analyses as well. Consumption regressions based on data from previous UNHS also show that type of employment is a significant independent predictor of household welfare, with employment in non-farm sectors having a greater effect on welfare (World Bank, 2006) **.** Although correlations between the growth of non-farm income opportunities and lower poverty, either over time in Uganda or at the household level in repeated cross sections do not necessarily imply causality, they do suggest _that the expansion of the non-farm enterprises in the informal sector was indeed good structural transformation for Uganda as it seeks to alleviate poverty_ .

# **6. Discussion**

Despite the importance of non-farm household enterprise incomes in increasing household welfare, it has to be recognized that most economic activities in non-farm enterprises use low capital and rudimentary technology. They are usually services, or the production of low quality goods. Therefore, labor productivity growth in this sector has limits and the risk of failure is also high. Although some hire external labor, many depend only on family labor input. Nearly 80 percent did not have a hired worker in 2005/06 while only 6 percent had three or more workers. When HEs are started as a secondary source of income, employment was not always full time **.** Around 44 percent of HEs (in operation for at least one year) reported operating for less than 12 months in a year, with 21 percent operating for less than six months in 2005/06 for example. In rural areas, it is common for households with non-farm enterprises to devote some weeks entirely to agriculture. About half of household enterprises seem to fail within 24
opportunity cost (labor market wage).<sup>7</sup> Our unique contribution is two-fold. First, we analyze long-term impacts of the school intervention on subsequent human capital and labor market outcomes. In the developing country context, when assessing impacts of an education intervention, the literature only uses outcomes measured only in education cycles (for example, change in test score at elementary school education), but we go beyond the school education cycles to investigate post-school outcomes such as labor market earnings.<sup>8</sup> The analysis required a unique tracking survey of students who moved out of their original school areas. Second, we show that impacts of an education intervention can depend on the features of labor markets. On this issue, we describe more details below.

This paper pays particular attention to gender, given the fact that females have traditionally been more educated in the Philippines. We focus on the gender gap in returns to schooling as a source of differentiation in the impacts of school quality improvement.<sup>9</sup> Figure 1.1 displays a clear difference in the returns to schooling for females and males, based on our estimation using the October 2009 round of the Philippine Labor Force Survey. Females experience higher (marginal) returns to schooling (that is, the slope of wage profile), especially above high school completion. The return function is steeper for females than males, which creates a greater incentive for females to study.<sup>10</sup> 7 Moreover, the program can have general equilibrium effects. When school quality improves, more children will be enrolled and the dropout rate also decreases, which alters the distribution of student abilities in the school. If low-quality students start to enroll, the average performance of students in school can be worsened. Therefore, the total effect of the intervention can be either positive or negative. 8 See, e.g., Hanushek (2006), Glewwe and Kremer (2006)
of changes in construction and manufacturing activity. It then develops a data-driven coreperiphery framework that allows for the delineation of polycentric cities as well as urban corridors. Finally, it uses the core-periphery framework to estimate changes within and across urban agglomerations. The last section summarizes our main results and provides concluding thoughts.

# **2. Booms and Busts: Analysis of Official Province-Level Data**

We begin by analyzing booms and busts using official sub-national data disaggregated by sector. We use administrative data because they are consistent with gross domestic product figures and match provincial boundaries. Our objective is to document changes in growth across the business cycle with a focus on understanding how sectoral changes interact with remoteness. To proxy for remoteness, we employ two intuitive measures. The first is the shortest distance from a province (centroid) to one of the two largest cities (Istanbul or Ankara). The second is the closest distance to a province (centroid) that has a major international port (Istanbul, Izmir, Bursa, or Mersin). While both measures are coarse, they capture proximity to domestic and external sources of demand respectively.

We show that the significant growth between 2010 and 2017 was bookended by recessions in which GDP per capita fell more sharply the closer a province was to one of the two largest cities. While, as may be expected, the declines in the manufacturing sector were more pronounced in the better-connected provinces during the 2008/09 recession (induced by the global financial crisis), declines in manufacturing were more pronounced in the remote provinces in 2018/19 (that may have been precipitated by decreases in domestic demand). However, the declines in the remote provinces were offset by a large increase in public spending (on defense, health, and education) during the 2018/19 recession.

This section is divided into three subsections. The first provides macroeconomic context and a broad overview of the changes in sectoral growth by period. The second documents the extent to which the less developed provinces grew more quickly than
distributions.

Should these inter-module differences in Gini coefficients in Table 7 be considered large? The difference between the Gini coefficient calculated with the benchmark diary and with module 1 is 14 percent and with module 5 it is 19 percent. These are equivalent to the average gap between consumption-based and income-based Gini coefficients reported by Deininger and Squire (1996), the basis for the current UNU/WIDER WIID database. Thus, just as those authors were careful to identify which inequality estimates came from consumption surveys and which from income surveys, so that investigators might adjust them to a consistent basis, so too would it be worth identifying which Gini coefficients are computed from surveys that use diaries and which recall, and over what reference period and level of respondent. Yet such meta-data are hardly ever reported, forcing investigators who combine inequality measures from various surveys to introduce considerable measurement error into their analyses.

> 24 The pattern of results also holds for Generalized Inequality measures GE(0), GE(1), and GE(2) (not presented but available upon request).
Section III utilizes data from a number of competitiveness reports to benchmark Latvia’s current progress against a number of comparator countries and to pinpoint Latvia’s strengths and weaknesses as an innovative economy. The analysis will suggest that Latvia is currently behind many EU New Member States (NMS’s) on many critical measures of innovative capacity. If Latvia is going to compete and prosper within the EU as well as in the broader global economy, remedying these weaknesses in the NIS must become an urgent priority.

Finally, Section IV offers a detailed list of policies and programs to improve the competitiveness of Latvian enterprises and the efficiency of the Latvian NIS. In particular, this section will highlight programs to improve (i) the production of knowledge generated by the Latvian R&D system; (ii) the commercialization of the knowledge produced by the Latvian R&D system; and (iii) the absorption and diffusion of knowledge produced outside Latvia and the ability of Latvian enterprises to integrate this knowledge into their production processes. The recommendations in this section are consistent with the broad policies and objectives which successive Latvian governments approved and endorsed in such documents as the National Concept on R&D (1998), the National Concept on Innovation (2001), the National Innovation Program (2003) and Priority 2 (Promotion of Enterprise and Innovation) of the Draft Programme Complement of the Single Programming Document for Latvia 2004-2006 (2003). Our primary contribution, therefore, is to help policy makers convert their broad policy objectives into concrete, specific programs that can produce tangible economic results in five years.

11
. The ENAMIN allows a relatively precise construction of a wide variety of variables that represent basic firm and entrepreneur characteristics: profits, employment size, capital stocks, time in business, and engagement with a wide variety of societal institutions, including those for which treatment effects are estimated in this paper. In addition, we are able to construct, from the ENEU, several variables representing personal characteristics of the entrepreneur and

> 8 See Heckman and Navarro-Lozano (2004) for a review of the different assumptions needed for each estimation procedure and recent development in control function semiparametric approaches.

11
Figure 2: Timeline: Baseline and Endline Data Collection <!-- Start of picture text --> Baseline<br>Movies<br>Movies + M−Learning<br>Follow−up<br>After COVID Follow−up<br>Dec 2018 Apr 2019 Jul 2019Sep 2019 05 Mar 202021 Mar 2020 05 Sep 2020 05 Dec 2020<br><!-- End of picture text --> have worked through, we also measured parental aspirations and expectations for their children’s futures, parental self-efficacy beliefs, perception of social norms and parental time allocation for home-learning activities. Finally, to study potential spillovers on siblings and other household members, the baseline and follow-up surveys collected data on school enrollment, learning outcomes, early parenthood, and early insertion in the labor market.

To measure the proportion of out-of-school children, we use two questions depending on whether follow-up data was collected before or after COVID-19 closures. Prior to March 2020, we asked parents if the target child was attending formal schooling (this represented 21% of the overall sample). For the remaining participants, and once the follow-up data collection resumed in September 2020, we asked a retrospective question about the child attending formal education between January-March 2020. See Section 5.5 for more information about the implications of COVID-19 and our robustness checks.<sup>9</sup>

> 9Because our main outcome on out-of-school children is based on parents’ responses, our measure might be subject to social desirability biases or experimenter-demand effects. Data checks of parental responses with school registry records were not possible in our study due to implementation and cost constraints. To address this potential issue, we collected a ‘wisdom of crowd’ measure that does not rely on direct information about the target child, and therefore it is less sensitive to such biases. The ‘wisdom of crowds’ measure aggregated the responses to the following question: “Now I would like to ask you about girls/boys like [Child’s name
took approximately four times the time required for GPS.<sup>7</sup> In Ethiopia, GPS required 13.9 minutes on average, while the compass and rope measurement on the same plots required an average of 57 minutes. In Tanzania, the duration averages were 7.4 minutes and 29.3 minutes for GPS and compass and rope respectively. These findings are consistent with previous studies such as Schoning et al. (2005) and Keita and Carfagna (2009) who find that compass and rope takes approximately 3.5 times as long as GPS on average.

To put the time considerations into context, given the sample size and average measurement durations in Ethiopia, the field teams spent a total of 416 hours measuring plots with GPS (1797 plots * 13.89 minutes) and 1,707 hours measuring with compass and rope. Using GPS instead of compass and rope, therefore, saved 1,291 hours of labor – over 160 person/days (at 8 hours per day). This estimate of time savings is for a relatively small-scale methodological experiment; savings in nationally representative household surveys would be proportionally larger. Minimizing the amount of time required to collect quality land area data can significantly reduce costs and improve the flow of fieldwork.

# **_B. Comparison of competing measurements_**

## _1. Compass and Rope vs. GPS_

In the literature, the main reservation regarding the use of GPS measurement in surveys is its performance on small plots. Furthermore, Keita and Carfagna (2009), Schoning et al. (2005), and Palmegiani (2009) all found that GPS tends on average to err on the negative side, i.e. to understate the area with respect to compass and rope.

Table 3 presents descriptive statistics on the GPS and compass and rope area measurements completed as part of the methodological studies. Mean plot size is small in all countries, ranging from 0.27 acres in Tanzania to 1.31 acres in Nigeria.
regional Agencies.

A search of the database can be performed either by trade category or beneficiary country or donor country or agency. The statistical analysis option allows quantitative analysis of trade capacity building data by these categories.

# _<u>COMEXT</u>_

Statistics on the trade flows between European Union Members and between each of them and their approximately 200 partner countries world-wide. Approximately 9,500 product groups based on the Combined Nomenclature, an eight-digit sub-division of the Harmonized System, can be monitored. Annual data from 1988 onwards and monthly data from January 2000 onwards. Produced by Eurostat, the Statistical Office of the European Union.

10 __________________________________________________________________________________________________________
in urban areas, partly because parents are better educated and the life expectations of young women are influenced by peer effects and access to better information.

The finding that lower caste urban women experienced more mobility compared to the upper caste women may in part reflect lower social constraints on their labor market participation. The caste difference can also be understood in terms of the insightful analysis of Munshi and Rosenzweig (2006) who find that the lower caste women were able to adapt better to the new occupations, as they are not expected to follow in their father‘s footprint. This ̳freedom by neglect‘ helps the daughters achieve better occupational mobility which is likely to feed into higher educational attainment. Using survey data from Mumbai, Munshi and Rosezweig (2006) find that the sons in lower caste families

> 44 As noted before, this is due to urban and skill biased nature of economic growth in post-reform India.

> 45 In the context of Malaysia, one explanation for higher educational mobility of daughters during the New Economic Policy is that parents invest in an older daughter‘s education in expectation that she in turn will finance the education of younger brothers by sending in remittances from high paying urban jobs.  In an environment of low financial deepening and negative real interest rates, this strategy may make a good deal of economic sense, especially when the returns to women‘s education is high in urban areas, as is the case in India (see Lillard and Willis (1994) on Malaysia).

29
.278***|
|(=0 in 2000)|(0.74)|(7.05)|(5.91)|
|n|1,378|1,392|1,378|
|R-squared|0.134|0.299|0.231|
|RMSE|4.75|8.03|7.97|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2012 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

There is a question of whether these results are unduly influenced by outliers. Robust regression is a technique that minimizes the weighted sum of squares of residuals, with observations weighted between zero and one based on Cook’s distance statistic, which measures the effect on the fitted values of deleting a particular observation. Outliers tend to have low (sometimes even zero) 7
|Based on UNPVSR<br>4/87|
|Germany, Federa|L May70|60651 J|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|Rep. of||||||
|Ghana|Mar84|12206 F|U.N. 1988 revision (prelim.)|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90,<br>Bank<br>projection|U.N. 1988 revision (prelim.)|
|Gibraltar|Nov 81|30 F|UNPVSR 7/86 (official<br>est.)|poetn||
|Greece|Apr 81|9740 F|UNPVSR 7/a6 (official<br>est.)|Eurostat<br>1987|Eurostat<br>1987|
|Greenland|Oct 76|50 J|UNPVSR 7/86 (official<br>est.)|||
|Grenada|Apr 81|89 F|Bank projection<br>from<br> <br>|Bank est.|Bank est.|
||||official<br>1983 est.|||
|GusdeC4'pe|Mar 82|327 J|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|Based on UNPVSR<br>4/87|
The findings of these serious studies are all well and good, and they suggest that further investments in PMGSY may well be socially profitable, but they do not establish as much. For what must be demonstrated is that spending yet more billions on PMGSY will yield a greater improvement in welfare than like spending on the next best alternative.

The plan of the paper is as follows. Section 2 deals with three key shadow prices (SPs), namely, the social discount rate, the premium on public income and the conversion factor for road-building. Section 3 describes the planned extension of the network and estimates the cost, at SPs, of a representative feeder road over an assumed 30-year lifecycle. Estimating the benefits is altogether much harder. In keeping with the three broad domains of commerce, education and health, Section 4 is split into three corresponding subsections. Various methods and sources are used, notably a survey of 30 villages in Orissa’s poor uplands (hereinafter, the Orissa Survey). Those forms of benefits that have defied my attempts to measure them are indicated and briefly discussed. The costs and measured benefits are compared in Section 5. If the former exceed the latter, the difference is the smallest contribution required of all non-measured benefits such that the project be socially profitable. In other words, the difference then measures the leap of faith involved in asserting such profitability. Section 6 takes up the program’s external effects on the wider economy, and I argue that, on balance, these will be beneficial. Alternative approaches to the measurement of benefits are briefly discussed in Section 7. The main conclusions concerning social profitability and the complementary policies needed to ensure it are drawn together in Section 8.

# **2 Shadow Prices**

Market prices depart from shadow prices for various reasons: market failure, externalities and, especially important in the present setting, the presence of distortionary taxes and subsidies as well as a social desire for greater equity than that yielded by market forces. There is no certainty whatever
then be used to inform training programs. This is discussed further in application #2 in the next section.

# **4. Application of Green Task Intensity to Indonesia**

The GTI index at the 4-digit occupational level can then be applied to datasets from countries collecting occupational data with ISCO-8. As an example, this section uses the case of a developing country, Indonesia, to calculate levels of green employment. It shows how different the results are when using the proposed methodology or O*NET data, either the O*NET GEP or the GTI calculated with O*NET task statements. Differences arise from the level of data collection in the country (4-digit)—which is the usual level collected in developing countries—and from the assumptions made when using crosswalks between taxonomies. Next, it describes the skills in demand by green jobs. Lastly, it applies the GTI (binary and continuous) to explore other green employment characteristics: gender and wage premium.

Green employment can be measured using the GTI index and Indonesia's labor force survey (Sakernas). In 2016, Sakernas started collecting occupational data using the national standard occupational classification system, _Klasifikasi Baku Jenis Pekerjaan Indonesia_ (KBJI) version 2014.<sup>57</sup> The KBJI 2014 corresponds to the adoption of ISCO-08 by Indonesia. However, two important warnings should be kept in mind. First, the illustration is done for Sakernas data for 2017, which is the latest year for which we have access to 4-digit occupational data.<sup>58</sup> Unfortunately, the shared micro data contains KBJI 2002 (based on ISCO-88) instead of KBJI 2014, for which the GTI was computed. Thus, the analysis needs to rely on a crosswalk for employment from KBJI 2002 to KBJI 2014. Such a crosswalk contains several many-to-many matches, which had to be split equally across matches. As a result, employment estimates could be inaccurate for these occupations. Nine out of the 35 green occupations are subject

>