rafmacalaba/data-use-mentions-extended huggingface dataset:
more a matter of _degree_ at the individual level than the absolute sector choice indicated by the above definition.

Adding to the confusion is that SSA countries tend to use conventional labor force surveys, designed for and well suited to the structured and monetized labor markets of OECD countries. When applied in SSA, these questionnaires can result in both outright omission of large segments of the working age population from the labor force, and underestimation of the extent of economic activity by individuals in a given sector. It can also result in the misclassification of the (primary) sector of economic activity because the sector where someone spends the most hours may not be the sector where the person creates the most value.

We quantified the extent of this measurement problem in Uganda using the 2005/6 UNHS household survey data, and we found it to be severe (Fox, 2009). First, we checked the importance of seasonality. Using the most commonly used recall period to measure of employment status – did you work in the last 7 days – resulted in a 10 percent smaller labor force than using a more appropriate recall period of 12 months. We found that the sector reported for the primary activity varied by whether the recall period was 7 days or 12 months. We also found that the use of screening questions in the 2005/6 survey dramatically increased the number of females and youth who reported that they were active in the labor force, compared with previous years. Finally, we found that 40% of the labor force had both a primary and a secondary economic activity, and these activities were in different sectors, suggesting that the classification of the employed labor force by primary economic activity would not pick up a major portion of the economic activity going on in Uganda. All of the 11
# **1. Introduction**

Between 2008 and 2018, disasters accounted for declines in crop and livestock production totaling USD 108.5 billion in low- and lower-middle income countries (LMICs), with the impact expected to further exacerbate with climate change (FAO, 2021). This toll is not only significant from an aggregate economic perspective, but its consequences are particularly dire in the poorest countries where over two-thirds of the population directly depend on agriculture for their livelihoods. Recent estimates by the FAO (2021) suggest that as much as 6.9 trillion kilocalories, equivalent to the amount it would take to end undernourishment among half of the undernourished population in low-income countries,<sup>2</sup> are lost to disasters diminishing crop and livestock production in the developing world.

With the impacts large in magnitude, widespread throughout LMICs, and likely to exacerbate, the accurate measurement of disaster-induced damages and losses<sup>3</sup> has been identified as a key component for economic development<sup>4</sup> and enshrined in Sustainable Development Goal 1.5.2 and Sendai indicator C. A large share of the disaster impact in LMICs accrues in agriculture and particularly due to reductions in pre-harvest crop production (FAO, 2018). Therefore, their accurate measurement at a micro-level is paramount to track progress toward the SDG and Sendai goals and inform strong disaster risk reduction strategies.

In a recent effort to provide a standardized methodology for measuring damages and losses in agriculture, Conforti, Markova and Tochkov (2020) propose the use of survey data to measure preharvest losses in crop production. However, there is yet scant evidence applying the method for micro-level loss measurement and a need for refinement to make full use of a variety of data sources including agricultural household survey data and geospatial data (FAO, 2018). This paper conducts a thorough descriptive assessment of the suitability of existing survey data to measure pre-harvest production losses of annual crops in LMICs at the micro-level. For this
and employment in the rural agricultural and nonagricultural sectors in Mexico. The analyses are based on ENIGH ( _Encuesta nacional de Ingresos y Gastos de los Hogares_ ) surveys from 1992 to 2002 and ENET ( _Encuesta Nacional de Empleo_ ) surveys from 1995 to 2003<sup>2</sup> . Rural poverty in Mexico is analyzed in depth in Verner (2005).

This paper is organized as follows. Section 2 addresses developments of the rural labor force, main changes in sectoral employment patterns, labor force characteristics,

> 2 The National Survey of Employment ( _Encuesta Nacional de Empleo_ , or ENE) was first collected in 1996. It contains information about the characteristics of the labor force in Mexico. ENE include variables at the individual level, for example education, hours worked, labor participation, contract type and benefits, social protection coverage, and other basic variables as gender, marital status, and age. Since 2000, ENE incorporates the National Survey of Urban Employment ( _Encuesta Nacional de Empleo Urbano_ , or ENEU). Starting in 2000 it became a quarterly survey and is renamed ENET ( _Encuesta Nacional de Empleo Trimestral_ ) and is reorganized to a panel survey that follows every single individual for 5 quarters. Since the second quarter of 2001, the ENET is representative at the national level, rural and urban level, 48 mayor cities, and Mexico’s 32 states.

1
efficiency gain obtained through taking advantage of the feature that informal business activity is clustered in certain areas. This method is primarily applied to study populations whose individuals tend to cluster and are usually hard to reach (Turk and Borkowski 2005, Heckathorn 1997). For such populations, standard random sampling methods are likely to result in large variances of population estimates due to likely misses of many clusters (Seber and Thompson 1994). Businesses, including those in the informal sector, may cluster in certain areas for multiple reasons, such as input-output linkages, social networks, or

> 5 Appendix Table A1 lists specific definitions of informality used in each country during the respective Informal Sector Enterprise Survey implementation.

5
br>St. Martin 5 6 5 11 2<br>Bermuda 3 4 3 2 4 4<br>Sint Maarten 5 5 5 11 2<br>Montserrat 9 7 3<br>Anguilla 5 4 4 3 1 2<br>Turks and Caicos Islands 8 1 2 2 2 3<br>Dominica 13 2 2 1<br>Martinique 11 2 1111<br>Aruba 10 1111 2<br>0 10 20 30 40 50 60<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from The National Hurricane Center's North Atlantic hurricane database reanalysis project (HURDAT2) created by the Hurricane Research Division, and NOAA's Atlantic Oceanographic and Meteorological Laboratory.

> 15 For more information, see <u>https://www.verisk.com/press-releases/2020/july/air-worldwide-releases-updatedtropical-cyclone-and-earthquake-models-for-the-caribbean/</u>

9
.262}|{.528}|{.051}|{.613}|{.114}|{.510}|{.741}|
|**Control Mean at BL**|48.8|15.1|42.3|26.4|-|8.02|32.5|59.6|30.9|-|
|**Observations**|2,381|2,381|2,381|2,381|2,381|2,400|2,400|2,400|2,400|2,400|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. Time allocation data was collected both at Baseline and Endline. Respondents were provided a set of 25 beads and a board with 6 circles representing: "Learning Activities", "IGA", "Leisure", "Household Chores", "Sleep" and "Other". The Learning category includes schooling, vocational training and study time. "IGA" includes paid and unpaid work of any kind. Respondent were then asked to allocate beads into each circle in a way that represents time allocation in an average day, and data points were later converted into weekly hours. The main results are estimated with an ANCOVA specification where the estimated SUR system includes six time allocation variables, the "Other Time Use" category is not reported. Control variables include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian
central Committee was responsible to identify ISIL suspects and refer them to the state judicial system, and the regional committees were tasked to prepare evidence of ISIL crimes and cooperate with local security actors. There is no reliable context data available regarding context changes as the agreement was signed while ISIL was still in power; however, after the area was retaken from ISIL, the agreement and context appeared to support safe IDP return to some degree.<sup>76</sup>

# **<mark>Peaceful Coexistence</mark>** **<u><mark>and Stabilization Agreement for Yathrib (2018)</mark></u>**

IDPs accused of ISIL affiliation were represented by their tribal leaders and did not engage directly in negotiations. Civil society was present at the signing and was given a role in the Committee postsignature. 11% of signatories were women. The Yathrib Agreement addressed issues related to security and justice, and focused on preventing retaliation via tribal justice (including collective punishments. The Agreement established a Committee comprised of religious leaders, community members, government actors and NGOs mandated to meet regularly to resolve conflict peacefully. However, this Committee does not appear to play a role in the return of IDPs or engagement with security or judicial actors in determining ISIL affiliation. The context after the Yathrib LPA seemed to improve and become more conducive of safe return.<sup>77</sup> 74 According to IOM’s DTM Master List, 1,500 IDPs returned within 2 months of the Tikrit LPA signature, and 63,360 IDPs returned within 6 months. No incidents of discrimination or conflict between IDPs and host communities was registered by the IOM Integrated Location Assessment 1 during this period.

75 By the time the Anbar Covenant was signed, IDP return had taken place at a high level: according to IOM’s DTM Master List, 405,696 IDPs had returned to the provincial capital Ramadi by roughly the same time as the LPA was signed
0.2|1.8|0.3|0.3|0.4|
|(Percentage points)|[-0.2, 2.3]|[-0.1, 0.7]|[-0.5, 5.4]|[-0.6, 0.9]|[-0.3, 0.9]|[-0.6, 2.2]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of macroeconomic and debt sustainability indicators across completed government debt booms with and without crises in respective country groups. Indicators on macroeconomic outcomes are annual averages during debt booms. In indicators on debt sustainability, initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. *, **, and *** denote that medians are statistically significantly different between booms with and without crises in respective country groups at the 10 percent, 5 percent, and 1 percent levels, respectively.

29
Elinder, M., J. Hagenc, M. Nordind and J. Save-Söderbergh. 2020. Who lacks pension knowledge, why and does it matter? Evidence from Swedish retirement savers. Institute for Evaluation of Labour Market and Education Policy, Working Paper Series, no. 2020:24.

- Elgin, C., M. A. Kose, F. Ohnsorge, and S. Yu. 2021. Understanding Informality. C.E.P.R. Discussion Paper Series, no. 16497, Centre for Economic Policy Research: London.

- EU-OSHA. 2016. Healthy Workplaces for All Ages: Promoting a sustainable working life. EU-OSHA: Bilbao.

- European Commission. 2012. Special Eurobarometer 378: Active Aging. January 2012. Retrieved at: <u>https://europa.eu/eurobarometer/surveys/detail/1002</u>

- European Commission. 2015. Discrimination in the EU in 2015. Directorate-General for Justice and Consumers: Brussels. Retrieved at http://dx.doi.org/10.2838/325154

- European Network of Public Employment Services. 2019. PES Strategies in Support of an Ageing Workforce: Study report. EU Publishing Office: Luxembourg.

- European Union Council. 2022. Council recommendation on individual learning accounts. 2022/C 243/03. Retrieved at: https://eur-lex.europa.eu/legal- <u>content/EN/TXT/PDF/?uri=CELEX:32022H0627(03)&qid=1664444877706&from=EN</u>

- Fairlie, R., S. Desai, and A.J. Herrmann. 2019. 2018 National Report on Early-Stage Entrepreneurship. Kauffman Indicators of Entrepreneurship. Retrieved at: http//www.kauffman.org/indicators

- Ferranna, M., JP Sevilla, L. Zucker and D. Bloom. 2022. Patterns of Time Use among Older People. NBER Working Paper Series, no. 30030.

- Fouejieu
inclusion yields very similar results to column (2).

It is also possible that HGFs are simply more prevalent in more innovative or more high‐tech sectors. In column (4) we control for this by the share of innovative firms at the industry‐ level, calculated from the Community Innovation Survey (only available for the whole country, rather than at a geographically disaggregated way). We find stronger productivity growth in industries with more innovative firms and in industries supplying more innovative industries. The regressions yield positive and mostly significant coefficients for the HGF measures, but they are substantially smaller than in the baseline model. Finally, in column (5), we control for the industry’s technology by including the average productivity of the vertically related sectors. This is calculated from our data, hence available at the same level of aggregation as the HGF measures. We find that firms supplied by and supplying industries with more productive firms experience higher productivity growth. The downstream HGF measure again becomes smaller but remains significant.

We find that HGF share in industries is indeed correlated with technology level and international integration. These industry‐level variables explain a significant part of the association between HGF share in vertically related industries and productivity growth, but even conditional on these factors more HGF share seems to be associated with a premium in productivity growth.

# **Table 10 Controlling for other industry characteristics**

(1) (2) (3) (4) (5) **Labor Productivity (3‐year change)**

**Dependent:**

19
11 survey, we construct the asset index based on the same categories of assets captured in every survey wave, ensuring full comparability over time. Most of these assets are recorded as dichotomous variables, taking 0/1 values, while the few categorical variables with multiple categories are manually reorganized along an ordinal scale according to costs. The asset variables are first normalized and then transformed into a single wealth index through principal component analysis, following an approach proposed by Kolenikov and Angeles (2009). Scree plots indicate that the first principal component is highly significant in every wave, while further components carry little information, as desired. The wealth-index is likely to indicate the long-term economic well-being, as many durables captured are typically held by households for many years and are not frequently replaced (Sahn and Stifel 2000).

In between the first and second survey waves an administrative reform took place in which the definition of urban areas was revised (Megill 2004), among other things. Some communities previously considered rural were now coded as towns, which, to some extent, explains the sharp increase in the proportion of urban population. As a consequence, results for urban areas are not comparable across the three RDHS waves in a strict sense.

# **4.2 Conflict proxies**

One obvious predictor of being a genocide victim in 1994 is ethnicity (that is, being Tutsi). Yet, only the 1992 pre-genocide RDHS wave records ethnicity, as self-reported by respondents. In an effort to suppress further ethnic tensions, the post-genocide government of Rwanda forbids the usage and identification of ethnic categories. Hence, RDHS collected after 1994 does not record respondents’ ethnicity.

In response to this challenge, we construct several ‘conflict proxies’ measuring likely exposure to conflict. These proxies allow us to differentiate two channels through which exposure to mass violence may influence fertility: replacement effects (where women choose to have children in the post-conflict period to compensate their lost children from during the conflict period) and marriage market effects (where a relative
in revenues and profits, but fails to reject no effect of outages on the revenues and profits of firms with employees. Turning to laborbased coping strategies, the study documents that one-person firm owners reduce their labor supply in response to outages. Firms with employees adjust labor in several ways, increasing worker labor hours on blackout days, increasing firm owner labor hours on days on which lights are restored, and decreasing dramatically total wages paid. Leveraging descriptive evidence on the composition of the workforce in the garment making industry in Ghana, this paper argues that firms employing unskilled workers drive the observed increases in worker labor hours while firms employing skilled workers drive the observed decreases in wages paid.

Taken together, these findings suggest that the economic costs of blackouts fall most heavily on singleperson firm owners, who experience large reductions in their effective wage, and on skilled workers, who

> experience large reductions in earnings. More detailed analysis of the distributional consequences of

> 20Appendix Table 8 estimates changes in capital from June 2014 to June 2015 using the retrospective follow-up data and fails to reject no effect on capital or capital composition. Point estimates are very near zero for both one-person firms and with workers.

14
Our results based on the non-structural empirical exercise are reported in the bottom panel of Table 4, and are not very different from those based on the calibration exercise. We obtain predicted net foreign asset positions of -3, -6, and -1 percent of wealth in the three scenarios, and the associated current account deficits are somewhat smaller than in the middle panel of the table.

While at first glance these current account deficits may seem large given our quite modest predictions for China's future net foreign asset position, it is important to remember that this predicted foreign asset share is applied to per capita wealth measured in international prices. This per capita wealth is very large relative to GDP at market prices, reflecting first China's low PPP exchange rate of about 0.25 in the Penn World Tables, and in addition the fact that projected wealth is between 1.5 and 2 times GDP at PPP. Thus a net foreign asset position of -5 percent of wealth in PPP terms 31
more likely to be family-managed in regions within countries characterized by low trust.

> 5Others have also worked with similar sized datasets. Claessens, Djankov and Lang (2000) look at 2,980 firms in 9 east Asian countries Lins (2003) covers 1,433 firms in 18 emerging markets. Faccio and Lang, (2002) use data on 232 big and medium-sized corporations in Western Europe. Franks, Mayer, Volpin, and Wagner (2012) span 27,684 large firms with more than 50mn Euros in sales across 27 European countries. Foley and Greenwood (2010) examine 2700 firms in 34 countries. In addition, most focus on family ownership rather than differentiating the effects of ownership from management.

> 6In particular, the WMS covers 8,481 firms across 18 countries with an average of 471 firms per country compared to 10,360 average firms per country in our database.

2
n|297|305|297|
|No. of countries|30|30|30|
|RMSE|4.29|4.02|3.85|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel > 25 percent of total exports. Country fixed effects are included in the regression. RMSE = root mean square error, or sample standard deviation.

28
The results of the analysis suggest that teachers’ subject-matter knowledge is important, and that 1.0 but also 2.0 standard deviation increases in teachers’ subject-matter knowledge are realistic medium-term targets for education policy making in Indonesia. A 1.0 and 2.0 standard deviation increase in teachers’ subject-matter knowledge across-the-board, is predicted to increase student-learning outcomes by 0.25 and 0.50 student-level standard deviations respectively by the time students complete the six-year primary cycle. These effects are substantial and would amount to 17.5 to 35.0 points respectively on the scale used by PISA if such impacts would persist until age 15.

The paper is organized as follows. Section (2) presents the teacher test score data and provides an intuitive account of the level and spread of subject-matter knowledge among Indonesia’s primary school teachers. Section (3) presents the statistical value-added model and presents ways of dealing with the difficulties of estimating the parameters of such models, e.g. these difficulties are due to measurement error in test scores and the time persistence of unobserved inputs. Section (4) presents regression results of three different value-added models: the standard level model, the school fixed-effects model, and a student-teacher fixedeffects model. Section (5) concludes and discusses the findings within the broader context of Indonesia’s education system.

# **2 How much do teachers know?**

The level of teachers’ subject-matter proficiency in Indonesia is said to be particularly low on average, even though rigorous international comparisons do not exist. Muhammad Nuh, for example, the country’s former Minister of Education and Culture reflected on the results of a large scale competency assessment of Indonesian teachers in 2012: “.. on one hand I am happy with the plethora of poor results because it means the data is reliable (The Jakarta Globe 2012).” Low levels of subject-matter proficiency can be a real constraint to effective teaching, even if teachers have a talent for delivering a message to a classroom.
# **CHAPTER 1: FINANCIAL ACCESS IN COLOMBIA**

## **1.1 INTRODUCTION**

1. The present report is part of a series of recent World Bank studies which explore the theme of access to financial services for low-income populations.  To date, regional efforts include work on financial access in urban Brazil (Brazil: Access to Financial Services, 2003), in Mexico (Broadening Access to Financial Services Among the Urban Population: Mexico City’s Unbanked, 2003) and in Nicaragua (The State of Microfinance in Nicaragua, 2002).  World Bank studies and operations on financial access have also been conducted in India (Access to Finance in India, 2004) and Romania (Romania Rural Finance Program).

2. The World Bank has worked with the Colombian government over the past years to address the challenge of low penetration of banking services for the poor. To this extent, a comprehensive study has been conducted on access to finance in rural Colombia (Colombia Rural Finance, Access Issues, Challenges and Opportunities, 2003). In addition, a conference jointly organized by Asobancaria, the Colombian Banking Assocation and the World Bank on ‘ _Bancarizacion – Una Herramienta para el Desarollo’_ in Cartagena de Indias in April 2004 raised awareness of the importance of access to financial services with Colombia’s private banks, regulators and politicians. Finally, the World Bank’s recently approved Second Programmatic Financial Sector Adjustment Loan to the Republic of Colombia includes a component on improvements in the provision of housing finance for the poor.

3. In this context, the present report focuses on access to financial services in Bogotá and urban Colombia, drawing on existing publications and internal reports, a demand side survey and an initial overview of supply side issues. This report also highlights a number of issues requiring further research. Due to the parallel efforts of the World Bank, rural finance and housing finance in Colombia are not included in the scope of this report.

4. The term unbanked has traditionally been applied