rafmacalaba/data-use-mentions-extended huggingface dataset:
Quadrant: Progress since<br>1st Quadrant: Both2010<br>2005 is better than average, but<br>performance in 2010 is worse Dominican Rep performance and progress<br>since 2005 are better than<br>than average 15<br>average<br>10<br>: FCSs<br>Peru<br>: Non-FCSs<br>Mongolia 5 Rwanda<br>Cambodia<br>Burkina Faso<br>Albania Brundi Benin<br>0<br>-20 -15 -10 -5 0 5 10 15 20<br>DRC<br>Bolivia -5 Bangladesh<br>Afghanistan<br>Cape Verde<br>-10<br>4th Quadrant: Both2010 2nd Quadnrat: 2010 performance<br>performance and progress -15 is better than average, but the<br>since 2005 are worse than progress is worse than average<br>better than average<br>-20<br><!-- End of picture text -->

> 12 Based on FY05 Low-Income Countries under Stress (LICUS) 13 Appendix 3 includes data used for Figure 4.

12
countries, respectively, in contrast to 71 percent in high-income countries and 80 percent in lower-middleincome countries.

**Figure 7. Countries adopting good regulatory practices on e-commerce/e-transactions by income group (in percentages)**

<!-- Start of picture text --> High income Upper middle income<br>Lower middle income Low income<br>Existence of e-<br>commerce law<br>100%<br>80%<br>60%<br>Legal equivalence of<br>Digital ID system to<br>40% paper based and<br>access e-government<br>electronic<br>services 20%<br>communications<br>0%<br>Technological<br>Legal recognition of<br>neutrality of electronic<br>electronic signatures<br>communications<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

Digital authentication allows users to be verified, uniquely and securely, through digital technologies or biometrics.<sup>14</sup> Digital IDs allow users to effectively access sensitive applications, data, and e-services, which enhances equality and inclusion.<sup>15</sup> A digital ID system to access e-government services is found in 30 percent of lower-middle-income countries and only 6 percent of countries in the low-income group; the system is adopted by 93 percent and 62 percent of high- and upper-middle-income countries, respectively. Singapore’s SingPass is a well-functioning digital ID system.<sup>16</sup> Users can access SingPass on their smartphones, logging in through biometric data, using their digital identity to sign e-documents or access more than 200 e-government services, such as tax or property information.<sup>17</sup> Instruments like SingPass allow citizens to interact easily with government services and save on administrative costs and fees.<sup>18</sup>

> 14
CONSUMPTION SUB-AGGREGATES 2 on other, more readily available data sources and consumption prediction methods, we explore whether less comprehensive consumption measures, or consumption sub-aggregates, that are also easier and less expensive to collect, might not suffice as an information base to track poverty over time. We draw on demand theory to establish the conditions under which this might be the case, and subsequently explore empirically whether these conditions hold in practice given the experience from a couple of different country settings.

The idea is inspired by Lanjouw and Lanjouw (2001) who imagine predicting poverty with food consumption only. Provided that Engel’s law holds (so that the food share is larger for the poor than for the nonpoor) and given a particular method of adjusting the poverty line when moving between expenditure aggregates,<sup>2</sup> the Lanjouws demonstrate that the Foster-Greer-Thorbecke (FGT) family of poverty measures will yield weakly smaller measured poverty when one uses only food expenditures rather than all expenditures.

This raises the question: Under what circumstances can one construct consistent poverty measures using different expenditure aggregates?<sup>3</sup> We extend the Lanjouws’ result, and show that so long as household welfare can be summarized by total non-durable expenditures, then one can construct consistent welfare measures using smaller aggregates provided that the Engel curve (quantities vs. total expenditures) for the sub-aggregate is linear.

Accordingly, we search for sub-aggregate expenditures that are linear in total expenditure. However, if relative prices within the subaggregate change, then the resulting sub-aggregate expenditures are only guaranteed to _remain_ linear if all of the constituent goods in the aggregate also have linear Engel curves. Thus, the key to constructing a robust proxy for total expenditures that can be used to track changes in poverty over time when prices are changing is to identify particular goods that have linear Engel curves that are fairly insensitive to changes in prices.

We use LSMS data from Rwanda, Tanzania, and Uganda and devise a simple way to identify goods with (nearly) linear
- 21 -

45. Under-investment in housing exists when the user cost of capital in housing'is significantly higher than the user cost of capital in the rest of the economy. This would indicate that policies are preventing a balanced flow of resources across sectors. The working hypothesis is that there are two main factors raising the user cost of capital in the housing sector above the opportunity cost of capital<sup>in</sup> the economy. First, Lhe interest rate on private financing reflected<sup>by</sup> curb or unregulated market interest rates (UMM) which<sup>more than</sup> compensates for the high rate of net capital gains. Second, the relative price of housing whose value has been raised by<sup>financial</sup> policies, land policies as well as high transaction costs which include large transfer taxes. So far in Korea, property taxes and other forms of user taxes are not high by international standards. Maintenance costs and rates of depreciation are not known to be especially high and tend to balance each other out.

46. What was a likely outcome when the paper was originally written has been confirmed by the recent work of Kyung-Hwan Kim (1987). He estimated that small changes in green belt boudaries for Seoul (by one kilometer) would increase developable land by about 14 percent and lead to a 2.7 percent decrease in housing prices; the elasticity of housing prices with respect to land supply being estimated at -0.2.

47. Similarly, Kim could show that policies were preventing the flow of funds into housing. Using a housing market survey for Korean cities of 1982 he found that the estimated real rate of return to housing was on the order of 8 percent for single family units and 12 percent for multi-family units. These rates considerably exceeded annual average real rate of return on corporate bonds of 5.1 percent by 3 to 8 percentage points.14/

# B. Skewed Size Distribution of New
e.g., Gordon et al., 1998; Okuyama and Chang, 2004; Hallegatte, 2008); (iv) Computable General Equilibrium (CGE) models at the regional or national level (e.g., Rose et al., 1997; Rose and Liao, 2005, 2007; Tsuchiya et al., 2007); or (v) network‐production system model, even though this line of research has not been operationalized yet (Haimes and Jiang, 2001; Henriet and Hallegatte, 2008). Available source of data for loss assessment are summarized in Annex 1.

# _Data collection on past events_

A first line of assessment consists of data collection on past events. This approach considers a single event in a single location. For instance, using firm‐level surveys, Kroll et al. (1991), Tierney (1997), and Boarnet (1998) investigate the consequences of lifeline and transportation interruption of firm activity and survival for the Loma Prieta earthquake in 1989 and the Northridge earthquake in 1994. This line of 16
Chiovelli, G., Michalopoulos, S., Papaioannou, E., and Sequeira, S. (2021). Forced displacement and human capital: Evidence from separated siblings. _National Bureau of Economic Research Working Paper Series_ , (w29589).

- Colombian Spatial Data Infrastructure (2023). Colombia shapefiles.

- Consejo Nacional Electoral (2023). Resultados electorales 1998–2018.

- Datosmacro (2024). Venezuela - producci ́on de petr ́oleo.

44
and over time. As future rounds of data collection are completed, the database will allow researchers to provide empirical evidence linking financial inclusion to development outcomes and promote the design of policies firmly based on empirical evidence.

The complete economy-level database, disaggregated by gender, age, education, income, and rural or urban residence, is available at http://www.worldbank.org/globalfindex. Individuallevel data will be published in October 2012.

1.  See, for example, King and Levine (1993); Beck, Demirguc-Kunt, and Levine (2007); Beck, Levine, and Loayza (2000); Demirguc-Kunt and Levine (2009); Klapper, Laeven, and Rajan (2006); and World Bank (2008a).

2. The Bill & Melinda Gates Foundation funded three triennial rounds of data collection through the complete questionnaire. In addition, data on two key questions relating to the use of formal accounts and formal loans will be collected and published annually.

3. For example, Beck, Demirguc-Kunt, and Martinez Peria (2007); and Cull, Demirguc-Kunt, and Morduch (forthcoming).

4. Collins and others 2009.

5. For a detailed literature review, see World Bank (2008a) and references therein. Campbell (2006) also provides an overview of the household finance field.

6. Beck 2009.

7. Johnston and Morduch 2008.

8. Dupas and Robinson 2009, 2011.

9. In addition, the World Bank has designed surveys to assess financial access in developing economies including Brazil, Colombia, India, and Mexico.

10. The LITS includes high-income economies in Europe and Central Asia. For additional information, see EBRD (2011).

11. See Beck and Brown (2011) for a discussion of the use of banking services in transition economies using the LITS data set.

12. See Beck, Demirguc-Kunt, and Martinez Peria (2007). In addition, Honohan (2008) and World Bank (2008a) used these indicators as
borders were better integrated than those within countries as a result of the common ethnicity of informal traders in the border vicinity. Similarly, Walther et al. (2019) conducted field surveys of 490 entrepreneurs in the West African rice value chain and found that social networks were of key importance for economic outcomes. Their econometric analysis showed that women were less central than men in business networks and that women’s income was significantly lower after controlling for age, experience, education, religion and matrimonial status. Another study on informal trade looked at the motivation for SSCBT using survey data for Benin and found that merchandise that was subject to high trade barriers was more likely to be traded informally rather than formally (Bensassi et al., 2019).<sup>3</sup> The present study also relates to the literature on the role of cooperatives in economic development. Cooperatives can serve as platforms for collective action through which smallproducers and traders can reduce transportation costs, disperse the costs of marketing, and negotiate more favorable prices. They can also be an institutional vehicle for policy makers to deliver direct benefits to small-scale producers, such as subsidized agricultural inputs. Yet, whether cooperatives help the poor and reduce poverty is a debated issue. For example, Bernard and Spielman (2009) analyze rural producer organizations in Ethiopia and find that poorer farmers tend not to participate in these organizations. Similarly, Fischer and Qaim (2012) and Blekking et al. (2021) study farmer groups in Kenya and Zambia, respectively, and find that wealthier households are overrepresented in cooperatives, and that cooperatives widen the rural poverty gap.

This paper uses a new dataset on SSCBT to build on and extend previous research on small-scale trade and on cooperative economic arrangements. Indeed, to the authors’ knowledge,

> 3 The data on informal trade reported in Bensassi et al. refers to survey findings from non-official border crossings and contains smuggling activity.

4
Table 6.2. Individual and household estimates by the unweighted, current UNPS and <u>calibrated GWSM base estimators</u>

||Unweighted<br>|UNPS<br>|Calibrated<br>GWSM<br>base<br>|Offi<br>|cial Statistics<br>|
|---|---|---|---|---|---|
||Mean|Mean|Mean|Mean|Source|
|Individuals||||||
|Female|51.1|50.8|51.1|51.0|Census (2014)|
|% children below 18 years old|51.2|52.1|55.0|55.0|Census (2014)|
|Literate population (+10 yrs)|80.7|80.8|81.7|72.2|Census (2014)|
|Working population|76.8|76.1|80.1|78.8|UNHS (2016)|
|Poverty headcount|20.4|19.3|19.9|21.4|UNHS (2016)|
|Households||||||
|Share of urban households|25.3|25.1|22.9|25.0|Census (2014)|
|Size of the household|4.8|5.0|4.8|4.7|Census (2014)|
|Share of agricultural households|78.7|80.1|79.4|80.0|Census (2014)|
|Number<br>of<br>rooms<br>in<br>the<br>household|2.2|2.3|2.1|2.4|UNHS (2016)|
|Household owns a cellphone|74.0|
- 10 -

**VIQiRR**

# SHARE OF HOUSING INVESTMEN **T** O GROSS DOMESTIC PRODUCT

<!-- Start of picture text --> 9.i<br>84<br>71 0<br>0~<br>u U S R dd G_-0 H<br>N<br>T 4 J/<br>01<br>G 3,0 0~~~~~~~<br>1000 2000 3000 4000 ScoD 6000 7X00<br>1975 PER CAPITA GROSS DOMESTIC PROOUJCT<br><!-- End of picture text --> <!-- Start of picture text --> (1981 U.S. DOLLARS)<br>S4Uute 41&iJ legend: Sue luuziluLt III text.<br><!-- End of picture text --> Notes to Figure 1: Data on GDP per capita were based on official excLange rates in 1981. The "normal" relationship portrayed moreover is **based** on substituting into the estimated equation mean values of population-related variables for Europe and North America# rather than for all countries on which the equation was estimated. Socialist countries in the sample are indicated by the following letters: C = Czechoslovakia, H = Hungary, P = Poland, R = Romania, and Y = Yugoslavia. Greece, shown for comparison, _ib_ indicated by the letter C.
understandable that the ratio would be higher for a teaching training program implemented once with some refresher sessions than for the provision of an additional year of education.

Panel B uses the more conservative discount rate of 5 percent to translate lifetime earnings into a present value (Haacker et al., 2020). We still find benefit-to-cost ratios all above 1. The minimum value for this ratio is 14.2 for the Gallego et al. (2021) study, which assessed the impact of a change in the math curriculum. The maximum value, which is for the Wolf et al. (2019a) study, is still quite large at 49.7.

# **5. Discussion and conclusion**

This study investigates whether current investments in preprimary education are too low through a systematic review of quasi-experimental evidence from around the world. Using 798 outcomes extracted and standardized from 54 studies, we use robust variance meta-regression to assess the returns to investment in preprimary education by establishing whether on average there is demand for preprimary programs and the extent to which preprimary education improves children’s skills during the preprimary period. To more concretely gauge returns, we also combine estimates from the literature on the relationship between improvements in cognitive skills and future earnings with study-specific estimates of improvements in these skills in a subset of studies from low- and middle-income countries that report per-child costs.

We find that interventions that expand access to preprimary education led to significant increases in the take-up of preprimary education services and school participation during the preprimary period. On average, preprimary education programs significantly improve children’s cognitive and social-emotional skills and executive functions in the short run, suggesting that these kinds of services promote learning and skill development better than children’s alternative care options during the preprimary period. A translation of these effects into gains in children’s earnings suggest sizable benefit-to-cost ratios, ranging from 1.7 to 103.5.

Although fewer studies track children beyond the preprimary years, we find evidence of smaller but statistically significant persistent effects of preprimary
Figure 5: Coefficient plot of PISA-based management index x math PISA points, by country <!-- Start of picture text --> E Europe & Central Asia East Asia & Pacific Latin America & Caribbean<br>Middle East & North Africa Southeast Asia W Europe & N America<br>60<br>40<br>20<br>0<br>Coefficient on management<br>-20<br>LIE TUR TAP PER BEL PRT CHL SRB JPN DEU QRS AUT QCN LUX LTU SGP RUS MNE NLD CHE THA GRC LVA ISL ROU BRA CZE URY COL IDN BGR ESP ITA ARG CAN MEX NZL FIN HKG MYS JOR CRI HRV KOR IRL EST SWE AUS KAZ FRA HUN SVK TUN DNK ALB VNM NOR USA ARE MAC QATPOL ISR GBR SVN<br><!-- End of picture text --> Note: PISA 2012 data. Regressions are estimated using OECD’s repest command in Stata, by country. The specification includes all five plausible values for PISA 2012 and _student final weights_ . Each marker represents the coefficient (and vertical spike represents associated 95% confidence intervals) of the management index on **math** scores.

37
# _I.INTRODUCTION_

Trade liberalization has produced in the past two decades steady growth in imports of intermediate and capital goods across countries. The endogenous-growth literature has provided theoretical arguments for the role of foreign intermediate inputs in enhancing economic growth and productivity gains (Ethier, 1979, 1982; Grossman and Helpman, 1991; Rivera-Batiz and Romer, 1991).<sup>2</sup> The specific influence of trade in capital goods on economic growth has also been emphasized in a number of theoretical and empirical works (Lee, 1995; Eaton and Kortum, 2001; Goh and Olivier, 2002). Importing capital goods is found to be a relevant channel of foreign technology transfers and R&D spillovers across countries (Xu and Wang, 1999). Trade liberalization is therefore expected to improve economic growth, by decreasing the cost of both foreign intermediate goods and capital equipments (Amiti and Konings, 2007; Topalova and Khandelwal, 2011; Goldberg et al., 2010; Eaton and Kortum, 2001).

This paper investigates the link between input-trade liberalization and foreign technology adoption embodied in imports of capital goods. Input-trade liberalization may affect technology adoption through a _direct_ channel: the reduction of tariffs on capital goods decreases their price and allows firms to import a larger volume of these goods. In this work, we take a different perspective and focus on an _indirect_ channel. We look at the effect of tariff cuts affecting variable inputs on firms’ decision to upgrade foreign technology in imported capital goods. We emphasize unexplored mechanisms through which trade liberalization affects firms’ technology choice: a supply shock of input tariff reductions and a complementarity channel between imported variable intermediate goods and capital equipment. Such complementarity is observed in our micro-data of Indian firms used in the empirical analysis. We first show that only a subset of firms in our sample import capital goods and almost all of them also import intermediate inputs. This feature of the data suggests that importing capital goods is associated

> 2Recent firm-level studies have confirmed that input-trade liberalization played a key
and Mahmoudi (2013), who decomposed poverty changes during the period 2000 to 2009 into growth and redistribution components, poverty declined between 2000 and 2004 and increased afterwards both in rural and urban areas. This is in line with findings in Mahmoudi (2011) who also reported an increase in poverty between 2004 and 2007.

There is, however, little knowledge about poverty trends in the Islamic Republic of Iran in the recent past, in particular after 2008, when the country’s economy entered a turbulent period as a result of energy price reform, the tightening of international sanctions, and falling oil prices. The most recent international poverty estimates reported by the World Bank and based on the group data from the Household Expenditures and Income Survey (HEIS) contain only two data points, for 2009 and 2013.<sup>4</sup> According to this source, poverty fell between 2009 and 2013 regardless of the choice of poverty line. Inequality, measured by the Gini coefficient and based on expenditure per capita, also declined, from 42 to 37.4 percent. This limited empirical evidence should be treated with utmost caution because it is based on two points in time and could mask volatility in poverty rates especially given the shocks the Islamic Republic of Iran has experienced during this time. In particular, during this period the Islamic Republic of Iran went through sanctions, removal of energy subsidies and initiated the distribution of universal benefits through cash transfers. Therefore, constructing a consistent and reliable poverty trend by including all years is essential to better understand welfare changes in the Islamic Republic of Iran after 2008.

This working paper fills the existing knowledge gap by constructing welfare aggregates and measuring poverty rates in the Islamic Republic of Iran using publicly available data from the Household Expenditure

> 3 In particular, the rural daily poverty line in 2004 was set at 7,679 rials and the urban one at 10,886 rials.

> 4 Datt (1998) discusses how group data can be used to calculate poverty rates.

2
2: 529‐549.</mark>

- <mark>Scheinkman Jose A. and Aureo de Paula. 2011. “The Informal Sector: An Equilibrium Model and Some Empirical Evidence from Brazil.”</mark> _<mark>Review of Income and Wealth</mark>_ <mark>57, S1: S8‐ S26.</mark>

- Schneider, Friedrich and D.H. Enste. 2000. “Shadow economies: Size, causes, and consequences.” _Journal of Economic Literature_ 38: 77–114.

- <mark>Schneider, Friedrich, Andreas Buehn, and Claudio E. Montenegro. 2010. "Shadow Economies all over the World: New Estimates for 162 Countries from 1999 to 2007."</mark> _<mark>World Bank Policy Research Working Paper Series</mark>_ <mark>.</mark>

- <mark>Yuki, Kazuhiro. 2007. "Urbanization, informal sector, and development."</mark> _<mark>Journal of Development Economics</mark>_ <mark>84, no. 1: 76‐103.</mark>

- World Bank. 2015. _Brazil Skills and Jobs_ . Latin America and Caribbean Regional Study, edited by Joana Silva, Rita Almeida, and Victoria Strokova.

- World Bank. 2014. World Bank HDNSP Pensions Database: Active Coverage 3Q2014. Washington, DC: World Bank.

World Development Indicators, latest available. Washington, DC: World Bank.

28
|Table 6. Effect of public schools provision on an|average household education spending|
|---|---|
|MMWPv|6274.88|
|MMWPu|1080.92|
|Effect or "savings'|5193.96|
|_Source:_Own calculation<br>based on ENIGH 96<br>1/ Annual 1996 pesos||

Assuming that there are no differences in the quality of education between a public and a private school, it is seen in table 6 (after controlling for observed and unobserved factors) that family's savings from sending a child to a public school amount to approximately $5,000 pesos per year (0.56 minimum wages in 1996). In addition, the results suggest that such savings are correlated with the schooling of the household head, location and number of children at home.

Now, assuming that the difference between private and public schools students' scores is only 10%, then, ninety percent of the effect or "savings" is due to relative payments and unforeseen expenses. The rest will reflect the amount that the average child would have to pay for "quality difference" in moving from a public to a private school.

The marginal willingness to pay desegregated by area is shown on table 7. Supposing that the quality between public and private schools in both rural and urban areas is the same, then, it turns out that the government provision of public schools is higher in urban areas than in rural areas. This result could be explained among other things, by the following factors: (i) poor location of public educational services; (ii) distance that an individual has to travel to the nearest school; (iii) the population dispersion and lastly the opportunity cost of the children in rural areas.

On the other hand, assuming that there is not a quality difference between private-public schools in rural areas and the quality difference between public-private schools in urban areas is a little above 50%, the