rafmacalaba/data-use-mentions-extended huggingface dataset:
method were then appended to create a single panel data set covering all countries, waves, remote sensing sources, and extraction methods. Table 5 summarizes the scope of the resulting data.

The Data Analysis Group then conducted all of the analysis on the blinded data set, posting the results to arXiv.org on 19 August 2021. On 23 August 2021, the Data Generating Group shared the key so that the Data Analysis Group could de-anonymize the data. Version 2 of this paper (arXiv:2012.11768v2) refers to all results based on their randomly assigned identifier ( `x0, ..., x9` ; `rf1, ..., rf6` ; and `tp1, ..., tp3` ). The current version of this paper (version 3) presents the same analysis as version 2 but replaces the randomly assigned identifiers with the actual extraction methods and names of remote sensing sources.

# **2.4 Descriptive Statistics**

Based on the combined household and weather data, we provide some general descriptive statistics on each of the two data sources, broadly defined.

> 7For more details on the definitions of growing seasons in each country, see Appendix A.2 and Table A1.

13
.407|0.407|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Cell FE||Y|Y|Y||Y|Y|Y|
|Controls X Time|||Y|Y|||Y|Y|
|Climate Controls||||Y||||Y|
|Mean Conflict|0.0635|0.0635|0.0635|0.0635|0.0233|0.0233|0.0233|0.0233|
|Each observation corresponds to a 50km x 50<br>in a given year, as reported in the ACLED data<br>include log of population at the start of the p<br>agricultural livelihood strategies. Standard er<br>p<0.05, *** p<0.01.|km grid-cell observe<br>set. Irrigated area is<br>eriod as well as du<br>rors in parentheses|d in a given year bet<br>measured as the per<br>mmies to indicate the<br>clustered at the grid-|ween 1998 and 2017.<br>centage share of an a<br>presence of wetland<br>cell level. Climate con|The outcome variable i<br>verage plot of land that<br>s, croplands, pasture l<br>trols include mean te|s a dummy variable in<br>is equipped for irriga<br>ands, rivers and the h<br>mperature and log of t|dicating that the gr<br>tion, as documente<br>omelands of ethnic<br>otal rainfall in the|id-cell experience<br>d by Siebert et al (<br>groups that histo<br>wet and dry seaso|d a battle event<br>2015). Controls<br>rically relied
~~4~~ _r_ + _εhrd_ where _Food_ _~~A~~ ccess_ denotes either the household FCS or rCSI; _Post_ _~~B~~ lockade_ is an indicator equaling one if the household was interviewed after November 6th, which is the day the blockade began; and _h_ , _r_ , and _d_ refer to household, governorate, and day of interview respectively.<sup>40</sup> Similar to specification (1), estimates of _β_ 2 denote the percent increase in food access following the blockade in all non-IPC 4 regions, and estimates of _β_ 3 denote the additional percent increase in IPC 4 regions.

Additionally, the November 2017 survey also included a special module on access to basic services. Thus, we also estimate specifications that use indicators equaling one if the household was experiencing deprivations in access to electricity, water, and municipal garbage collection as the dependent variables. Lastly, we also estimate a specification using an indicator equaling one if the household suffered from three or more of the four deprivations measured (including poor FCS) simultaneously as the dependent variable.

Estimates of specification (2) are presented in Table 4. Consistent with Tandon and Vishwanath (2020), the estimates demonstrate that both food access and access to basic services did

> 39The analysis above relied on monthly summary statistics at the governorate level as opposed to the individual-level data. Special permission was obtained to use the one month of individual-level data.

> 40Given the use of a single month of individual-level data, the governorate fixed effects absorb the conflict variables that were used as controls in specification (1).

15
growth in %)**

<!-- Start of picture text --> $4,500 23%<br>4,176<br> $4,000<br>18%<br> $3,500 18.3%<br> $3,000 13%<br> $2,500<br>8%<br>8.8%<br> $2,000<br> $1,500 3%<br>1,150<br>3.1% 1,042<br> $1,000 0.8% 634 712<br>516 -2%<br> $500 -2.1%<br>-3.3%<br> $- -7%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Millions<br>Growth (in %)<br><!-- End of picture text --> Source: author’s calculations Closing the gender pay gap within sector would increase GDP by 4.8 percent (Figure 4). Liberia is not an exception to the global disparity that exists between male and female remuneration at work and especially in Sub-Saharan Africa (UN Women, 2016). Whereas some of such disparity can be explained by observable attributes of the female workforce such as attainment in education, the gender pay-gap is also reflective of wage discrimination between men and women. In Liberia, male workers earn 6.55 percent

> 24 The value adopted is 1.95 and sourced from the UN Population estimates given that the same source is used for Liberia and SSA estimates, yet this differs from recent estimates given by the OECD which put the average in 2019 to 1.6 children per woman (OECD, 2021).

18
2. Capital costs, _RKi_ .

3. Revenues, _Ii_ .

4. Labor shares, _θi_ .

As in Hsieh and Klenow [2009], labor shares can be inferred from US inputoutput tables. As for firm-level or industry-level data, there are a variety of datasets that include these variables for different countries. For example, Hsieh and Klenow [2009] use India’s Annual Survey of Industries (conducted by the Indian government’s Central Statistical Organization) for India, and the Chinese Annual Surveys of Industrial Production (conducted by the Chinese government’s National Bureau of Statistics) for China. Other private and more standardized datasets may also suffice for our purposes. For example, the AMADEUS dataset includes firm-level data for all European (and some Eastern-European) firms.

Note that we do not take a stand about the sources of the frictions. In other words, we just calculate the marginal products of labor and capital in tradable and non-tradable sectors as they are. To test for “supply shifts” such as subsidies on the tradable consumption sectors, we will calculate if the marginal products of the tradable consumption sectors are systematically lower than those of the nontradable sectors. To test for “demand shift” such as subsidies on the investment good sectors, we will calculate if the marginal products of the investment good sectors are systematically lower than those of the consumption good sectors.

# **5.4 Identifying real exchange rate misalignment based on the revenue-cost ratio**

A potential problem with the Hsieh and Klenow [2009] approach outlined above is that it relies on knowledge of the production functions, and in particular, the capital and labor shares. For our purposes, this is problematic as, in reality, inputs are not homogeneous; the “labor” used in the production of tradable goods need not be the same as the “labor” employed in the non-tradable sectors. Further, production technologies may differ across countries, and taking this heterogeneity into account is likely to a complicated task.

38
10 occupations. For Costa Rica and Panama, some recoding is needed to map workers to the same occupational taxonomy.

Besides the number of workers in each _cohort-gender-occupation_ category, I also need the years of schooling, labor income, and hours worked associated with them. Hours worked in the main occupation are reported weekly in each survey, while income is reported on a monthly basis. Years of schooling and labor income are coded by the SEDLAC. The labor income is deflated using the consumer price index (2011=100). Beyond this general transformation, each country presents its own idiosyncrasies, which are discussed below.

**Costa Rica:** There are two different surveys available for the country in SEDLAC. For 1990-2009, the repository contains the surveys from _Encuesta de Hogares de Propósitos Múltiples_ (NISC, 2009) and from 2010 onward the surveys correspond to _Encuesta Nacional de Hogares_ (NISC, 2020). The sampling methodology varies a bit across the two surveys which raises the concern of consistency. However, in practice there are no unusual jumps in the time series of variables around 2010. The yearly change from 2009 to 2010 in the series appears reasonable given the past variations.

Beginning in 2001, the household surveys in Costa Rica adopted the ISCO-88 occupational taxonomy. Before 2001, a national classification was employed. The system was based on the second 36
that older women in the U.S., whose longer life spans make them more vulnerable to old-age poverty, are less financially literate than the older population as a whole, and less likely to plan for retirement.

# **_Financial literacy follows an inverted-U shape with respect to age_**

Financial literacy tends to peak among adults in the middle of the life cycle, and it is usually lowest among young people and the elderly. In the U.S., for example, those in the prime age group (25-65) tended to do about five percent better on the questions than those under 25 or over 65. Lusardi and Mitchell (2011a) note the difficulty of distinguishing between age and cohort effects in a single cross-section, but hypothesize that individuals likely accumulate knowledge over time which then decays as they age. Finke et al. (2011) likewise attribute lower financial literacy in older people to a decline in cognitive processes associated with old age, and rule out the effects of cohort and other demographic characteristics. A deeper examination of financial literacy among older Americans can be found in Lusardi and Mitchell (2009a).

However, there is a much larger literature on financial literacy and its determinants among young people, given low levels of literacy and the perceived need to equip young adults with the ability to manage their own finances. Lusardi et al. (2009b) find that less than a third of American teenagers (ages 12-17) possess basic knowledge of interest rates, inflation, and risk diversification. Women, African-Americans and Hispanics, and those with lower educational attainment are associated with lower levels of financial literacy. Mandell (2006) notes that there is even evidence that youth financial literacy has been declining since the late 1990s, based on the Jump$tart surveys administered to high school seniors. Beal and Delpachitra (2003), a study of Australian college students, also found low levels of financial literacy.

Various factors may account for different levels of financial literacy among youths. Valentine and Khayum (
social insurance scheme on the SRB, Ebenstein and Leung (2013) use (a) multivariate regressions to see whether change in SRB before and after 1991 was associated with program availability in the village, and (b) complement this with an instrumental variable (IV) model that instrumented variation in the program’s availability using the share of the village’s population ages 40 or more. The data for this analysis came from the 2002 China Household Income Survey and the Chinese census. The evaluation results suggest that the SRB increased more in counties that had fewer villages participating in the program, and the increase in SRB was 9 percent less in counties that did adopt the program even after controlling for other village-level characteristics.

Ebenstein (2014) finds similar results for Korea and Armenia. For details on the Korean study, see Section 5. For Armenia, Ebenstein (2014) tests how the collapse of the Soviet Union, and the withdrawal of social protections provided under the USSR, affected the male fraction of births. Using data from the Armenian census, he employs a difference-in-difference approach to compare the male fraction of births had by high educated and low educated parents, before (1983-1991) and after the Soviet collapse (1992-2001). The idea behind this empirical strategy is that low educated parents would be particularly vulnerable with the sudden withdrawal of social protection, and they would therefore be more likely to sex select and revert to traditional forms of old-age insurance. The difference-in-difference estimate suggests that after the fall of the Soviet Union, the male fraction of births for low educated parents increased by 2.2 percent. These results do not necessarily imply a causal relation between the availability of social insurance and the tendency to sex select (more details in following paragraph). However, the timing of the increase in SRB, and the group that particularly contributed to that increase in SRB, hint at the plausibility of a causal relationship.

Caution must be used when interpreting the results outlined above. A causal relationship between social protection and
# **Can a Small Social Pension Promote Labor Force Participation? Evidence from the Colombia Mayor Program**

_Tobias Pfutze_<sup>1</sup> Florida International University _Carlos Rodríguez-Castelán_<sup>2</sup> The World Bank

**Keywords:** Pensions, Labor Force Participation, Colombia **JEL Codes:** H55, J08, J26, O15

**Sector Board:** Poverty and Equity Global Practice (POV)

The authors would like to thank Carlos Casteñeda, Sara Kotb, and Daniel Valderrama for helpful research assistance. This paper has benefited from helpful comments and suggestions by Wendy Cunningham, Giselle del Carmen and Robert Zimmermann. The authors are also grateful to seminar participants at the 2015 Summer Initiative for Research on Poverty, Inequality, and Gender of the Poverty and Equity Global Practice at the World Bank for their useful comments and suggestions. The authors would also like to thank Juan Carlos López and Sandra Díaz at the Colombia Mayor Implementation Agency (Consorcio Colombia Mayor), and Alejandra Corchuelo and Guillermo Rivas from the Ministry of Planning (Departamento Nacional de Planeación, DNP) for providing useful data employed for some the analysis. 1 E-mail: <u>tpfutze@fiu.edu (corresponding author)</u>

> 2 E-mail: <u>crodriguezc@worldbank.org</u>
>9<br>1<br>16|14% (9%)|
|50-59|Public formal|5<br>2|**84**<br>1<br>8|45% (40%)|
||Unemployed|41|25<br>34|1% (1%)|
||Out of labor force|30<br>5<br>8|**57**|7% (15%)|
||Private info|rmal<br>Private formal|Public formal<br>Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to men who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by age groups. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

14
, and generally benefits society by providing a combination of goods and services whose qualities and attributes are adapted to the demands of consumers using up as small a quantity of resources as possible in the supply of these products. Competition also makes enterprise expansion profitable due to the productivity gains that it stimulates.

> 6 Using data on German manufacturing and service-sector firms from the third Community Innovation Surveys (CIS3) for the period 1998-2000, Peters (2005) finds that product innovations have a net positive impact on employment while process innovations are associated with employment reduction for manufacturing but not service firms.  These findings are largely confirmed by Harrison et al (2008) in a study that is also based on CIS3.  Using comparable firm-level data across four European countries—France, Germany, Spain, UK—they find that process innovation has significant displacement effects that are partially counteracted by compensation mechanisms.  The displacement effects of process innovation are most pronounced in manufacturing.  On the other hand, product innovation is associated with employment growth and these results are similar across countries.  Based on a firmlevel comparison across provinces and cities in China, Mairesse et al (2009) find that the compensation effects of product innovation  more than counterbalance the displacement effects of process innovation, the net result being that innovation makes a strong positive contribution to total employment growth.  Alvarez et al (2011) find that in the case of Chile, process innovation is generally not a relevant determinant of employment growth, and that product innovation is positively associated with employment growth.

> 7 Syverson (2011) provides a deep survey of principal recent work on the determinants of enterprise-level productivity.

7
The transition process did not simply lead to demand and supply shocks. It also led to a deep institutional restructuring. It is plausible that the transition process, by removing the existing coordinating mechanisms, led to an increase in search costs, and in turn to a decline in aggregate activities. Some Bank surveys-for example, Ickes and Ryterman (1994), Qimiao Fan (1994), De Melo (1 997)-have attempted to measure these changes. The data collected however suffer of the limitations emphasized above, focusing mainly on few of the aspects of the transition process.

Another dimension of particular interest to policy-makers is firn responses to macro shocks. Since this type of analysis has a less defined set of theoretical hypotheses and may be more specific in scope, it is difficult to evaluate the coverage of the World Bank surveys. Instead, we discuss the advantages and disadvantages of recent surveys undertaken in response to macro-economic crises.

Research on transition economies mentioned above is a natural starting point since they provide some information on how firns react to shocks or changes in the environment in which they operate. Earle, Estrin and Leshchenko (1996), for example, focus on the privatization process and its impact on firm performance. Using survey data collected in Russia in 1994, the authors analyze the effects of different ownership structures on firm behavior. Their findings suggest that outsider-owned and state-owned firms differ significantly in terms of perforrnance and but less in terms of restructuring behavior. The latter result may be partly a function of the timing of the survey (it was conducted only two years into the transition process) and, consequently, the restructuring process had made only limited progress.'<sup>4</sup> Recanatini and Ryterman (1999a and 1999b) study the reaction of firns to the institutional vacuum left by the removal of government institutions. Their analysis suggests that, because of the sudden loss of information and the underdeveloped legal system, firms reacted by by an underdeveloped banking system, initially postponed the inevitable restructuring process.
(s/y)' = private savings ratio adjusted for domestic capital gains - Source: WB "World Savings Database" Rev. 3.00.

**yt'=** GNDI adjusted for domestic capital gains - Source: WB "World Savings Database" Rev. 3.00 + our calculations

**govsia=** public sector saving rate adjusted for domestic capital gains - Source: WB "World Savings Database" Rev. 3.00.

39
Figure 4. Dynamic treatment effects on average wage reported by the firm <!-- Start of picture text --> Soft letter Strong letter<br>Filing period Filing period<br>.15<br>.15<br>.1<br>.1<br>.05<br>.05<br>0<br>0<br>Marginal effect on avg. wage (w.r.t control) Marginal effect on avg. wage (w.r.t control)<br>-.05<br>-.05<br>Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> Source: Authors' calculations using historical tax declaration records. Coefficients from random effects model plotted.

# Considering heterogeneity of impacts based on sector of activity

Beyond the differential impact of the notification letters due to characteristics of the firm in terms of whether the owners themselves were explicitly targeted for suspicion of under-declaration, an important factor to consider in this context is the sector of economic activity of the firms subject to these notifications. Some industries would have been deemed as essential (e.g., food services) and others as non-essential (e.g., entertainment venues), and thus influence the operational capacity of firms and with it (a) access to postal mail and (b) the ability to retain employees. How these factors influence reactions to strategic notifications is seemingly ambiguous: on the one hand, essential businesses (such as grocery stores) that would have stayed open and not necessarily limited capacity would have for the most part “received the message” (employers and employees), but have been too busy to react. On the other hand, non-essential businesses (such as travel agencies) were much less likely to get the message in the first place, but also potentially more sensitive
considered countries is 10 years and 3 months, with a variation between countries of at most 5 months.

While virtually all children in developed countries are enrolled in fourth grade, this is not necessarily the case in developing countries. If a substantial part of the potential student population of a country is not enrolled in school in fourth grade, this might introduce sample selection bias relative to full-enrollment countries. The most detailed data on this are Filmer’s (2003) enrollment profiles based on national survey data, which include Colombian data for 2000. The data show that while 98.5% of Colombian children have been enrolled in first grade, this declines to 91.6% in fourth grade. While the rich and middle class, as well as the urban population in general, come relatively close to universal enrollment at fourth grade, this is not true for the poorest 40% of the Colombian population, or for the rural population. Even though more than 97% of the poor and of the rural population in Colombia enroll in first grade, early drop-out rates among them are very large. At the age of 15-19, only 83.4% of the poorest 40% of the Colombian student population, and only 81.4% of the rural Colombian population, report that they were enrolled in fourth grade (Filmer 2003; cf. Pritchett 2004). This selective non-attendance at the primary-school level may somewhat distort the Latin American samples of students tested by PIRLS, although the enrollment data show that the effect can be expected to be limited.

The PIRLS sampling procedure ensured that a representative sample of the target population was tested in each country. PIRLS used a two-stage sampling method, sampling in the first stage schools and in the second stage intact class(es) of the school. This let to a sample size of 3,300 students in Argentina and 5,131 students in Colombia. In the comparison countries we analyze, the sample size ranges from 2,494 to 7,633 students.
|250|0 (see notes)|
||11.2%|0%|

_Source:_ Ulaanbaatar City Administration, and the survey of kiosks conducted by the author.

## **Value Added from Kiosks in Ulaanbaatar**

119. The estimates of value added were compiled in a straightforward manner, using the responses to survey questions and extrapolating for the population of kiosks in Ulaanbaatar. Each kiosk in the survey provided the average earnings after subtracting the cost of inventories, transportation, and rent, for both the average weekday and for the average weekend day. These daily averages were then weighted for each kiosks for the number of weekdays and weekend days that the kiosk is usually open for business to arrive at an average weekly value added for each kiosk. The sample mean for average weekly value added is 47,900 togrogs. Extrapolated to a year for 2,229 kiosks in Ulaanbaatar yields an estimate of value added of approximately 5.55 billion togrogs per year at current (3/97) prices.